39 jobs found

Do you want to do work that matters, along side supportive leaders who will help you grow faster than you ever thought possible? Are you a creative problem solver who is energized by challenges? You've come to the right place. YOUR IMPACT You will collaborate with clients and interdisciplinary teams to develop advanced analytics solutions, optimize code, and solve complex business challenges across industries. You'll deepen your expertise by contributing to cutting edge projects, R&D, and global conferences while working alongside top tier talent in a dynamic, innovative environment. You'll partner with clients to understand their needs and develop impactful analytics solutions. You'll translate business challenges into analytical problems, build models to solve them, and ensure they are evaluated with relevant metrics. Additionally, you'll contribute to internal tools, participate in R&D projects, and have opportunities to attend and present at leading conferences like NIPS and ICML. Your work will create real world impact. By identifying patterns in data and delivering innovative solutions, you'll help clients maintain a competitive edge and transform their operations-driving measurable, lasting improvements across industries. You'll be based in London and collaborate closely with Data Scientists, Data Engineers, Machine Learning Engineers, Designers, and Product Managers worldwide. Together, you'll work on interdisciplinary projects to solve complex business challenges across various sectors. Partnering with QuantumBlack, AI by McKinsey leadership, client executives, and technical experts, you'll lead the design and deployment of advanced machine learning and AI solutions that deliver tangible business impact. You'll thrive in an unparalleled environment for growth. You'll connect technology with business value, tackle diverse challenges, and collaborate with inspiring multidisciplinary teams, gaining a holistic understanding of AI's transformative potential while advancing as a technologist and leader. YOUR GROWTH Driving lasting impact and building long term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture-doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward. In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues-at all levels-will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won't find anywhere else. When you join us, you will have: Continuous learning:Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast paced learning experience, owning your journey. A voice that matters:From day one, we value your ideas and contributions. You'll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes. Global community:With colleagues across 65+ countries and over 100 different nationalities, our firm's diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you'll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences. World class benefits:On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well being for you and your family. YOUR QUALIFICATIONS AND SKILLS Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Applied Statistics, Mathematics, Engineering, Physics, or other technical fields 2+ years of professional experience applying machine learning and data mining techniques to solve real world problems with substantial data sets Programming experience (focus on machine learning): SQL and Python's Data Science stack; good knowledge of at least one big data framework (e.g., PySpark, Hive, Hadoop) is a plus. R, SPSS, and SAS are considered nice to have Strong understanding of machine learning methods and experience applying them to complex, data rich environments Ability to prototype and deploy statistical and machine learning algorithms, and translate analytical outputs into data driven solutions Experience deploying ML/AI technologies into production or applied business environments is a plus While we advocate using the right tech for the right task, we often leverage: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro (our open source data pipelining framework), Dask/RAPIDS, Docker, Kubernetes, and cloud solutions such as AWS, GCP, and Azure Familiarity with Generative AI (GenAI) and agentic systems is a strong plus Excellent time management skills to handle responsibilities in a complex and largely autonomous environment Willingness to travel Strong communication skills, both verbal and written, in English with the ability to adapt your style to different audiences and seniority levels FOR U.S. APPLICANTS: McKinsey & Company is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by applicable law. FOR NON U.S. APPLICANTS: McKinsey & Company is an Equal Opportunity employer. For additional details regarding our global EEO policy and diversity initiatives, please visit our McKinsey Careers and Diversity & Inclusion sites.

Overview Position Overview: As a Forward Deployed AI Engineer within Convatec's AI Centre of Excellence, you will help turn AI opportunities into practical, production-ready solutions that improve how our business works. Embedded directly with business teams, you will take ownership of AI-enabled workflows from discovery and design through to build, testing, deployment, monitoring and handover. This is a senior, hands-on delivery role for someone who can work independently across architecture, integration, DevOps, MLOps, AIOps, data engineering and governance. You will make technical decisions, solve complex integration challenges and ensure AI solutions are reliable, scalable and safe to run in live business environments. You will work with technologies such as Microsoft Copilot Studio, Microsoft Fabric, Azure DevOps, Azure AI Foundry and SAP Joule, helping to design, build and deploy AI workflows that connect into real operational processes across Convatec. We are looking for someone who is comfortable being the senior technical voice on an initiative: setting standards, guiding others, managing technical risks and ensuring solutions are successfully handed over to the teams who will use and support them. Responsibilities Workflow implementation and engineering: Design, build and deploy AI-enabled workflows using Azure AI Foundry, Microsoft Copilot Studio, Microsoft Fabric and SAP Joule. This includes agent orchestration, automation triggers, prompt integration, exception handling and reusable delivery patterns. API integration and service connectivity: Connect AI workflows to enterprise systems through REST APIs, event streams and Microsoft Fabric data pipelines, ensuring secure and reliable data flows across platforms such as SAP, Salesforce and Convatec's data lake. Technical leadership and standards: Act as the senior technical voice within embedded initiatives, setting engineering standards, guiding Applied AI Engineers and business teams, and making sound architecture and integration decisions within agreed guardrails. Testing, validation and quality assurance: Plan and execute functional, regression and edge-case testing, including failure scenarios, fallback paths, escalation triggers and data quality checks. Assess when workflows are ready for production release. Rapid prototyping and feasibility assessment: Build timeboxed prototypes in the AI Landing Zone sandbox to test technical options, demonstrate value and provide clear go/no-go recommendations before full delivery. DevOps, MLOps and AIOps ownership: Implement and maintain CI/CD, monitor and release pipelines using Azure DevOps, including version control, rollback capability, automated testing and production health monitoring. Documentation and operational handover: Produce clear technical documentation and handover materials so business and operational teams can maintain, monitor and extend AI workflows after delivery. Decision-making authority: Make technical recommendations on workflow architecture, integration patterns, tooling choices, production readiness and handover quality within the scope of each initiative. Skills & Experience Essential: Minimum 4+ years' experience in software engineering, AI/ML workflow delivery or systems integration, with a demonstrable track record of full-stack delivery in production AI environments. Minimum 2+ years' operating in a senior or lead technical capacity, making independent architectural decisions and guiding other engineers without formal line management authority. Hands-on, production-grade experience across DevOps, MLOps and AIOps - CI/CD pipeline ownership, model versioning, automated testing and live system monitoring - not as adjacent knowledge but as daily practice. Strong proficiency in Python and SQL; solid REST and event-driven API integration experience including enterprise systems such as SAP (via SAP Joule) or Salesforce. Hands-on experience with Azure AI Foundry, Microsoft Copilot Studio and Microsoft Fabric in production delivery contexts. Demonstrated ability to self-direct across the full delivery lifecycle - from discovery and design through to production deployment and business handover - without close technical supervision. Experience working directly with business stakeholders: translating operational requirements into engineering decisions, managing expectations and owning adoption outcomes. Desirable Experience with SAP Joule or integrating agentic AI solutions with SAP business processes. Familiarity with AI Landing Zone design patterns, guardrails and governance frameworks. Experience with containerization (Docker/Kubernetes) and cloud-native deployment patterns on Azure. Understanding of multi-agent coordination patterns using Copilot Studio or Azure AI Foundry. Knowledge of healthcare data privacy requirements including GDPR or HIPAA. Soft Skills Self-directed and comfortable working through ambiguity. Technically credible, with the confidence to set standards and challenge approaches where needed. Outcome-focused, with strong ownership of delivery quality and production reliability. Adaptable, able to work across multiple business areas and changing priorities. Clear communicator, able to explain technical decisions in a way that business stakeholders can understand and act on. Education/Qualifications Bachelor's or Master's degree in Computer Science, Software Engineering, Information Systems or a related field, or equivalent practical experience demonstrated through a strong delivery track record. Relevant Azure certifications (e.g. Azure Developer Associate, Azure Data Engineer Associate, Azure DevOps Engineer Expert) or MLOps/AIOps are desirable. Travel & Working Conditions Travel up to 10% of the time, mostly within Europe. Hybrid working model - 1 day per week on-site at our London office. Legal and Compliance Equal opportunities Convatec provides equal employment opportunities for all current employees and applicants for employment. This policy means that no one will be discriminated against because of race, religion, creed, color, national origin, nationality, citizenship, ancestry, sex, age, marital status, physical or mental disability, affectional or sexual orientation, gender identity, military or veteran status, genetic predisposing characteristics or any other basis prohibited by law. Notice to Agency and Search Firm Representatives Convatec is not accepting unsolicited resumes from agencies and/or search firms for this job posting. Resumes submitted to any Convatec employee by a third party agency and/or search firm without a valid written and signed search agreement will become the sole property of Convatec. No fee will be paid if a candidate is hired for this position as a result of an unsolicited agency or search firm referral. Contact:

About 9fin 9fin is the AI platform powering global debt markets - the world's largest asset class at over $145 trillion. Debt markets are vast, global, and mission-critical, yet still run on fragmented data, PDFs, and manual workflows. 9fin replaces this broken infrastructure with a single platform that centralises proprietary credit data, deep analysis, and high-value workflows across global markets. Today, 9fin powers teams at 300+ blue-chip institutions worldwide, including global banks, asset managers, private equity firms, law firms, and advisors. The business is scaling at exceptional speed, with rapid expansion in the US and best-in class retention driven by deep workflow adoption. We're at a defining inflection point. With proven product market fit and strong, global market pull, 9fin is accelerating toward becoming the category defining platform for debt markets worldwide. The Opportunity Our AI product area is growing. We already have two Product Managers working across conversational AI, agentic workflows, and domain specific automation, and we need a third to expand our capacity as the scope of AI at 9fin accelerates. Backed by over $170M in funding (Series C), we are scaling rapidly across product, engineering, and go to market. AI is not a bolt on feature at 9fin; it is the core differentiator for the next phase of the company. You will join an established AI PM team and take ownership of a product area within our AI surface. This could span new AI powered features for credit analysts, workflow automation for legal and financial analysis, or expanding our conversational AI into new domains. The team has already built evaluation infrastructure and quality frameworks; you will use and contribute to these rather than build them from scratch. You will work within our product organization (eight squads across AI, data, and domain verticals) and partner closely with ML engineers, data scientists, domain experts, and designers. This is a high autonomy role with real scope to shape what 9fin's AI products can do for our clients. What You'll Do Product Strategy & Execution Collaboratively create and champion a clear product vision and strategy for your area, with a focus on building and evolving our AI products Synthesize qualitative and quantitative insights into problem statements that identify root causes, not just symptoms Shape the next generation of agentic AI products: multi step reasoning workflows, tool orchestration, and autonomous task completion for financial professionals Demonstrate a working understanding of the challenges specific to building with LLMs, including managing hallucinations, evaluating output quality, and designing for non deterministic systems Use confidence building methods (prototypes, user research, data analysis) to understand how users will interact with AI powered products before committing to full builds AI Quality & Data Use the team's AI evaluation framework (LLM as judge protocols, quality thresholds, SME eval cycles) to validate and improve the products you ship Contribute to the data strategy for your product area: annotation quality, ground truth curation, and feedback loops that improve model performance over time Proactively identify and track metrics that measure client and business benefit, working with the team on continuous model evaluation and improvement Team Leadership & Communication Create a collaborative environment for your squad, providing wider context and clear goals so everyone can do their best work Work with data scientists and ML engineers to bridge the gap between business needs and the technical capabilities of AI models Communicate strategy, initiatives, and progress transparently across the organisation Translate complex AI concepts, capabilities, and limitations into language that non technical stakeholders can act on Champion responsible AI practices: transparency in model outputs, bias monitoring, and compliance with client expectations around AI generated content What We're Looking For 5+ years of product management experience, preferably in FinTech or B2B SaaS, with at least 2 years focused on ML or GenAI products Working knowledge of RAG architectures, embeddings, prompt engineering, and LLM evaluation methods (not just "AI concepts" at a high level) Experience shipping AI products where output quality is probabilistic: you know how to define "good enough" and iterate from there Comfortable reading Python notebooks and SQL to interrogate model outputs and usage data Track record of shipping AI products where you managed the tension between ML research/exploration and production delivery constraints Collaborative leadership style with strong stakeholder management; you build relationships that empower your team to achieve outcomes User centric communication skills: you can explain AI capabilities and limitations to clients and internal stakeholders without overselling or underselling You thrive in fast paced, ambiguous environments and are energised by the challenge of building something new Nice to Have Experience in financial services, legal tech, or data/analytics platforms Familiarity with credit markets, debt instruments, or regulatory/compliance workflows Experience with NLP applied to document analysis (contracts, legal filings, financial reports) Experience with agent orchestration frameworks, tool use patterns, or multi step AI workflows Benefits Competitive, market benchmarked salary Pension with 7% company matching Private medical insurance, paid sick leave, income protection, group life assurance Season ticket and cycle to work schemes Hybrid flexibility with up to 3 months annual work abroad 25 holiday days plus local public holidays (exchangeable) One month paid sabbatical after 5 years; enhanced parental leave Professional development budget and regular social events 9fin is an equal opportunities employer At 9fin we are dedicated to building and promoting a fair and inclusive workplace where everyone can reach their full potential and truly belong. We recognize that building diverse teams enables a more creative and productive environment. If you're excited about this role but your experience doesn't perfectly align with the job description, we encourage you to apply anyway. You might just be who we're looking for - either for this role, or perhaps another.

Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of 300,000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready? Job Overview: Infosys Consulting is at the forefront of applied AI innovation, delivering real-world business value through the convergence of AI agents, machine learning, and modern enterprise architecture. As part of our growing Enterprise AI consulting practice, we are looking for technically hands on professionals to design and deliver client centric intelligent systems and support business growth through strategic pre sales and solutioning initiatives. Key Responsibilities Design, develop, and deploy autonomous AI agent ecosystems using frameworks such as LangChain, AutoGen, CrewAI, and Semantic Kernel. Architect LLM powered workflows involving multi agent collaboration, decision logic, memory management, and external tool integration. Collaborate with consulting teams to align AI agent solutions with business goals and industry use cases across sectors (FSI, Retail, Manufacturing, etc.). Participate in RFI/RFP responses, creating high impact solution overviews, architectural diagrams, and effort/cost estimations. Work closely with AI Strategists, Engagement Managers, and Domain SMEs to define solution blueprints, MVP scopes, and transformation roadmaps. Engage in client workshops, demos, and innovation showcases to articulate the potential of Agentic AI and its enterprise applications. Contribute to the development of reusable agent templates, accelerators, and reference architectures within Infosys' AI frameworks. Stay current with GenAI advancements, toolchains, and research (LLMs, embeddings, vector DBs, agent planning/reasoning). Provide technical mentorship and hands on support to junior consultants, helping shape internal capability development. Collaborate with cross functional teams on AI governance, responsible AI practices, and integration into enterprise environments. Required Qualifications Bachelor's or Master's degree in Computer Science, AI, or related field. PhD preferred for architect level roles. 8+ years of experience in AI/ML, including 5+ years as a Solution Architect and 4+ years of hands on development with LLMs and autonomous AI agents. Strong experience with Python and orchestration libraries such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar. Deep knowledge of LLMs (GPT, Claude, LLaMA, Mistral, etc.), prompt engineering, agent memory, tool calling, and autonomous task execution. Experience with pre sales, RFP/RFI support, and proposal creation in a consulting or enterprise services environment. Understanding of enterprise solutioning with cloud platforms (AWS, Azure, GCP), API integration, and data security best practices. Exceptional communication and consulting skills, with the ability to present solutions to both technical and non technical stakeholders. Preferred Skills Hands on exposure to cognitive architectures, planning based agents, or reinforcement learning in real world deployments. Experience integrating AI agents into enterprise apps like Salesforce, ServiceNow, SAP, or custom apps via APIs. Understanding of AI observability, performance monitoring, and ethical guidelines in GenAI systems. Given that this is just a short snapshot of the role we encourage you to apply even if you don't meet all the requirements listed above. We are looking for individuals who strive to make an impact and are eager to learn. If this sounds like you and you feel you have the skills and experience required, then please apply now. We offer industry leading compensation and benefits, along with top training and development opportunities so that you can grow your career and achieve your personal ambitions. Curious to learn more? We'd love to hear from you Apply today!

Build what's next in applied AI at JPMorganChase - where your work shapes how teams use intelligent systems at scale. You'll lead hands on engineering for agentic and GenAI capabilities that power the LLM Suite platform. This role offers a mix of deep technical problem solving, architecture ownership, and collaboration with talented builders. If you enjoy turning ambiguity into reliable production systems, you'll thrive here. Join a team that values craft, security, and learning. As an Applied AI ML Lead in LLM Suite Engineering, you will design and deliver production grade AI/ML and agentic solutions that integrate seamlessly with existing systems. You will own technical direction across architecture, implementation, and operational stability, with a strong focus on secure, high quality software. You will partner with peers across engineering to identify patterns and improve standards, reliability, and scalability. You will help evolve the platform using modern public cloud services and agentic frameworks. You will contribute to a collaborative culture through communities of practice and emerging technology events. You will explore and operationalize emerging patterns such as agent to agent communication, model context protocols, and agentic orchestration, turning early stage concepts into scalable, production ready capabilities. Job Responsibilities Design, develop, and troubleshoot software solutions using creative approaches to solve complex technical challenges Write secure, high quality production code and maintain algorithms that integrate with existing systems Create architecture and design artifacts for complex applications, ensuring design constraints are met through delivery Build AI/ML solutions and agentic systems for the LLM Suite platform using public cloud architecture (Azure, AWS) and modern agentic frameworks Implement GenAI services leveraging Azure OpenAI models and AWS Bedrock Identify hidden problems and patterns in data proactively to improve coding standards and system architecture Participate in software engineering communities of practice and events focused on emerging technologies Required Qualifications, Capabilities, and Skills Computer science degree or equivalent practical experience Hands on experience with system design, application development, testing, and operational stability Proficiency in Python (FastAPI) Experience building microservices and APIs Experience with elastic compute, NoSQL databases, and messaging queues Strong understanding of the Software Development Life Cycle Solid grasp of CI/CD, application resiliency, and security Preferred Qualifications, Capabilities, and Skills Experience implementing GenAI services leveraging Azure OpenAI models and AWS Bedrock Proficiency working with large language models and building agents with LangGraph Experience developing, debugging, and maintaining code in a large corporate environment using modern programming and database querying languages Experience with containerization Knowledge of agent to agent (A2A) communication concepts Familiarity with Model Context Protocol (MCP) Experience with agentic orchestrators, personal AI assistants, or AI skills development

Chubb is looking for a Senior AI Engineer to lead the design, development, and deployment of advanced generative AI solutions that improve how we operate across the insurance lifecycle - from underwriting and claims to customer service and risk assessment. This role sits at the intersection of software engineering, applied AI, and technical leadership, turning the latest LLM capabilities into reliable, production-grade systems built on our Azure and Databricks platform. As a senior member of the team, you will not only build high-quality AI solutions, but also help shape technical direction, guide architecture decisions, mentor other engineers, and influence how AI capabilities are delivered across the organization. Key Responsibilities: Lead the design and build of end-to-end generative AI applications using LangGraph, including agentic workflows, RAG pipelines, and LLM-integrated services. Define and drive architecture for scalable, secure, reliable, and maintainable AI solutions deployed in production. Develop robust, well-tested backend APIs with FastAPI, containerized via Docker and deployed on Kubernetes. Architect and own search and retrieval solutions using Azure AI Search and MongoDB, including design of indexing strategies, hybrid search configurations, and retrieval evaluation. Work within our Azure cloud environment, integrating with Azure OpenAI, Azure services, and our broader data platform. Collaborate with product managers, data scientists, engineers, and other stakeholders to scope and deliver AI features that address real insurance business problems. Own the full software lifecycle, including design, code review, testing, deployment, monitoring, and production support. Translate complex business requirements into well-scoped technical solutions with a strong focus on reliability, maintainability, failure modes, and operational excellence. Mentor and support junior engineers through design reviews, code reviews, and technical guidance. Contribute to technical standards, best practices, and reusable patterns for AI engineering across the team. Evaluate and improve model quality, retrieval strategies, prompt and agent design, and production performance over time. Partner with business and technical leaders to shape AI roadmaps and identify high-impact opportunities for automation and decision support. Qualifications Desired Qualifications: 6+ years of software engineering experience, with strong fundamentals in system design, data structures, and clean code practices. Proven experience designing and shipping production-grade AI or ML-enabled software systems. Strong proficiency in Python, including building and shipping production backend services. Hands on experience building applications with LLMs such as Azure OpenAI, OpenAI, Anthropic, or similar. Experience with FastAPI or comparable Python web frameworks. Production experience with Docker and Kubernetes, including deployment and management on AKS. Strong hands on experience with Azure, including Azure OpenAI, Azure AI Search, AKS, and Databricks. Proven production experience with LangGraph and LangChain, including multi step agentic workflows and tool calling agents. Hands on experience with Azure AI Search or other vector/hybrid search solutions, including vector search, hybrid search, and retrieval evaluation. Experience with Databricks and PySpark for large scale data processing. Experience designing evaluation frameworks for LLM systems, including offline metrics, hallucination detection, and production monitoring. Experience implementing responsible AI practices in a regulated environment, including audit trails, confidence scoring, and human in the loop controls. Background in traditional ML or data engineering, including feature pipelines, model training, and ETL, is preferred. MongoDB or other NoSQL database experience is preferred. Prior exposure to insurance domain concepts such as underwriting, claims, and policy administration is preferred. Experience mentoring engineers, leading technical discussions, and influencing architecture decisions. Preferred Attributes: Strong problem solving skills and the ability to work through ambiguity. Ability to balance speed of delivery with engineering quality and long term maintainability. Strong communication skills and the ability to explain technical concepts to both technical and non technical stakeholders. A proactive mindset with ownership of outcomes and continuous improvement. Interest in emerging AI technologies and practical application of LLMs in enterprise environments. What Success Looks Like: You lead the delivery of high impact AI solutions that are reliable, scalable, and production ready. You help the team make better technical decisions through strong architecture and engineering judgment. You raise the quality bar through mentoring, code reviews, and engineering best practices. You contribute to a stronger AI engineering capability across the organization. Join Chubb as a Senior AI Engineer and play a key role in shaping the future of intelligent, scalable, and enterprise ready AI solutions that create real impact across the insurance lifecycle. We offer in return! Competitive salary & pension scheme, discretionary bonus scheme, 25 days annual leave plus ability to purchase 5 additional days, hybrid working options, Private Medical cover, Employee Share Purchase Plan, Life Assurance, Subsidised gym membership, Comprehensive Learning & development offerings, Employee Assistance program. Integrity. client focus. respect. excellence. teamwork Our core values dictate how we live and work. We're an ethical and honest company that's wholly committed to its clients. A business that's engaged in mutual trust and respect for its employees and partners. A place where colleagues perform at the highest levels. And a working environment that's collaborative and supportive. Diversity & Inclusion. At Chubb, we consider our people our chief competitive advantage and as such we treat colleagues, candidates, clients, and business partners with equality, fairness and respect, regardless of their age, disability, race, religion or belief, gender, sexual orientation, marital status or family circumstances. We are committed to ensuring our recruitment process is inclusive and accessible to all. If you have a disability or long term condition (for example dyslexia, anxiety, autism, a mobility condition or hearing loss) and need us to make any reasonable adjustments, changes or do anything differently during the recruitment process, please let us know. Job Info Job Identification 34586 Job Schedule Full time Regular or Temporary Regular Job Category Analytics Engineering Business Unit United Kingdom Legal Employer Chubb European Group SE UK Branch

Senior Principle Agentic AI OrchestratorApplylocations: United Kingdomtime type: Full timeposted on: Posted 2 Days Agojob requisition id: JR111457 Be the one building AI-powered experiences where they matter most. At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships. Help build, support and operate technology used by more than 8,000 organizations in over 100 countries - moving AI from possibility to production in real-world enterprise environments every day. Agentic AI Orchestrator Professional Services AI & Digital Solutions EMEAAs an Agentic AI Orchestrator at Genesys, you will serve as the strategic and technical bridge between customer ambition and successful AI transformation.You will partner directly with strategic customers across the EMEA region - leading with a consultative approach, moving with the agility that enterprise AI demands, and measuring success against customer business outcomes. At Genesys, we're transforming how organisations connect with their customers through empathy, collaboration, and innovation.This role offers the opportunity to make a lasting impact by helping enterprises move from AI exploration to continuous transformation - and to shape how that transformation capability is built and scaled within the organisation. What You'll Do Advise, Influence & Drive Adoption Lead discovery and strategy alignment - partner with Genesys CX Advisors, Solution Consultants, and customer stakeholders to identify AI use cases, assess feasibility and value, and translate business KPIs into actionable technical priorities Surface the real problem beneath the presenting symptom - use data, evaluation outputs, and systematic analysis to form precise hypotheses and direct where intervention has the most leverage Influence adoption through credibility: when customers are blocked, uncertain, or sceptical, resolve it through evidence and clearly articulated reasoning Translate data-driven findings into executive-ready narratives - present complex AI performance insights, business impact, and recommendations with the clarity and confidence that influences C-level decisions Design and Architecture Define reference architectures, integration patterns, and data flows for AI-powered experience orchestration - adapting the approach iteratively as customer context and data reveals new priorities Lead process-redesign workshops to create seamless, channel-agnostic CX - facilitated with a consultative approach that builds customer ownership of the solution Ensure all designs comply with Genesys and customer security, privacy, and regulatory requirements (GDPR, PDPA, PCI, HIPAA where applicable) Prototype and Implementation Deliver rapid POC and MVP implementations using Genesys Cloud AI Studio, CoPilot, Agentic Virtual Agent, and related product suites - moving from hypothesis to working prototype at pace, and adjusting direction when the evidence requires it Integrate Genesys AI components with customer CRM, ERP, and third-party systems Establish implementation KPIs and analytics to measure model and journey performance from day one - not as an afterthought AI Engineering & Outcome-Oriented Delivery Design and implement evaluation frameworks to measure AI solution quality in production: intent accuracy, retrieval groundedness, response relevance, agent goal completion, and policy adherence Build automated eval pipelines that enable rapid, systematic iteration across prompt variants, guardrail configurations, and model versions Architect agentic systems with precision: define agent topology, design tool schemas, and engineer orchestration logic. Measure success through production adoption and demonstrable outcome improvement - use outcome data as the primary signal for where to focus next Optimisation and Continuous Improvement Define baseline metrics at engagement start and iterate relentlessly Evaluate solution performance against KPIs and refine designs based on data-driven insights, changing direction quickly when the data signals it Collaborate with Customer Success and Professional Services teams to hand over production-ready assets and roadmaps Codify field learnings into reusable frameworks, evaluation standards, and accelerators that scale capability beyond individual engagements Governance, Ethics, and Enablement Champion responsible AI design principles and apply guardrails to prevent bias or unsafe responses Adhere to Genesys ethical standards and compliance frameworks Mentor customer, partner, and internal teams to build long-term AI maturity and self-sufficiency - transferring expertise, not just delivering outcomes Feed well-formed, evidence-backed field signal to product and solution teams - precise enough to influence roadmap priorities directly What We're Looking For Experience Bachelor's degree (Master's preferred) in Computer Science, Information Technology, Data Science, or a related discipline 8-12 years of combined experience across AI implementation, CX/CCaaS platform consulting, or technical solution architecture - demonstrated through overlap and measurable customer impact, not additive year counts across separate tracks Extensive on-field experience implementing or supporting CX, CRM, or AI orchestration platforms (e.g., Genesys Cloud, Google CCAI, Salesforce, Microsoft, NICE CXone, AWS Connect, ServiceNow, or similar) Hands-on experience with agentic AI systems: building, evaluating, or operating LLM-powered agents in production contexts Demonstrated experience working with APIs, data pipelines, and modern cloud environments (AWS, Azure, GCP) Track record of mentoring or developing technical peers and codifying expertise into approaches others can build on Consultative & Customer-Facing Skills Proven autonomy inside complex enterprise accounts - leading discovery, shaping scope, managing stakeholder expectations, and building trust without hand-holding Comfortable operating without a defined playbook - able to form and test hypotheses under ambiguity, pivot quickly when the data signals a change, and bring stakeholders along through the process Translates data-driven findings into executive-ready narratives: quantified outcomes, causal relationships, and clear next steps - not qualitative summaries Proven leadership in cross-functional environments and complex enterprise contexts Product instinct: able to define success metrics, surface well-formed requirements, and articulate the business case for technical decisions Experience with industry verticals such as Financial Services, Healthcare, Insurance, Retail, or Public Sector Multilingual communication ability is an advantage across the EMEA region Technical Skills CX orchestration and workflow design across multiple platforms - with a focus on outcome over architecture elegance Conversational AI and Agentic Virtual Agent implementation across voice, chat, and messaging channels Knowledge engineering: retrieval system diagnosis, RAG pipeline design, semantic coverage analysis, and knowledge optimisation for AI consumption Agentic system design: tool schema authoring, multi-agent topology, prompt engineering as a systematic discipline, and guardrail implementation for enterprise-safe agent behaviour Applied data analysis: Python for evaluation scripting, log analysis, and integration development; SQL for operational data querying - AI-assisted development tooling expected and encouraged Deep working knowledge of one or more enterprise CCaaS or Agentic AI platforms - Genesys Cloud preferred, or equivalent such as Google CCAI, Salesforce, AWS Connect, NICE CXone, Sierra, Decagon, or Cognigy; demonstrated expertise in deploying and optimising conversational or agentic AI solutions on any of these platforms is equally valued Data and integration expertise: REST APIs, event-driven architecture, JSON Cloud infrastructure familiarity: provisioning, access control, and cost management (AWS preferred) Data governance, security compliance, and responsible AI design principles Working at Genesys AI at enterprise scale - Build, support and operate AI-powered technology used by more than 8,000 organizations worldwide. 150+ new AI features were released in the last fiscal year. A flexible-first culture - Join a global team of nearly 7,000 employees with flexible ways of working designed to help people do their best work. Growth in the AI era - Build future-ready skills through mentorship, learning programs, leadership development and education support. Time to recharge and give back - Benefits include paid volunteer time, August Free Fridays, well-being resources and regionally tailored programs for employees and their families. Recognized globally - Genesys is Great Place to Work certified in 17 countries and 94% of employees are proud to tell others they work at Genesys.Learn more about our culture, AI innovation and sustainability commitments through our Careers site and Sustainability Report. What Happens After You Apply After you apply, here's what you can typically expect: Our Talent Acquisition team reviews your application with the hiring team. A Talent Acquisition Partner will review your application and, if your background is aligned, schedule a Zoom interview. Next, you'll meet the hiring manager and other members of the interview team. We aim to keep the process focused and respectful of your time, with no more than five interviews in most cases. After interviews are complete, our team will follow up with the final steps.Every application is reviewed by a person. Response times may vary by role and location, but our team will keep you informed throughout the process . click apply for full job details

Senior AI Consultant Department: AI & Advanced Analytics Employment Type: Full Time Location: Bristol, UK Reporting To: Mark Todkill A few application tips Clear, specific examples of your leadership, client impact and technical delivery Relevant experience aligned with the seniority, consultancy focus and technical scope of the role A thoughtful response to any application questions A CV that highlights both what you did and why it mattered Why this role exists We are preparing for growth driven by upcoming projects with multiple key clients. While details are still being finalised, we know they will require senior AI talent with depth across traditional machine learning, data science and Gen AI. We are looking for someone who can operate quickly, lead confidently and build robust, production ready solutions. We are proactively expanding our team with a Senior AI Consultant who can shape complex work, guide delivery teams and act as a trusted adviser to clients. What you'll be doing You will shape and deliver AI solutions for real world business problems, spanning traditional machine learning, rigorous data science and practical Gen AI use cases. You will work with a small, highly capable AI team, collaborating with data engineers, consultants, technical leads and senior client stakeholders to deliver meaningful outcomes. Your responsibilities will include: Leading the design, build and iteration of machine learning and data science models to solve complex business challenges Identifying and shaping Gen AI opportunities, including LLM based workflows, agentic systems and applied AI prototypes where they add measurable value Providing technical direction, code quality oversight and delivery guidance across shared repositories and client facing work Managing client stakeholders, presenting recommendations clearly and translating technical outputs into practical decisions and actions In your first 3 to 6 months, success might look like: Taking ownership of ambiguous or novel technical challenges and setting a clear delivery approach for the team Delivering high quality traditional ML, data science and Gen AI solutions that align with client needs Communicating confidently with senior stakeholders about technical decisions, trade offs, risks and results Becoming a trusted partner to clients and colleagues within a lean, fast moving team that relies on autonomy, judgement and critical thinking What you'll need to succeed We are looking for someone who brings a thoughtful mix of senior technical expertise, consultancy experience, client leadership, team management, rigour and communication strength. You might be a great fit if you have: 5+ years of relevant experience delivering AI, ML or data science solutions end to end, ideally in a consulting environment Strong expertise in traditional machine learning and data science, including forecasting, segmentation, optimisation, classification or similar techniques Strong practical experience with Gen AI, including LLMs, RAG, agentic workflows, prompt evaluation, model selection or production implementation At least 3 years' experience managing client relationships and leading, mentoring or managing a technical team A strong mathematics or quantitative background, with solid programming skills in Python, SQL and Snowflake Excellent communication skills, with the ability to explain complex concepts clearly and confidently to technical, non technical and senior client audiences Nice to have: Experience shaping proposals, statements of work, roadmaps or discovery phases within a consultancy setting Experience building neural networks, transformers, LLM based architectures or production AI platforms This role might not be the right fit if you're looking for: A highly structured corporate environment with clearly defined processes, as we are lean, fast and self driven A fully remote role - regular in office collaboration in Bristol is required and highly valued by the team A role focused only on Gen AI; this position requires strong capability across both traditional machine learning and Gen AI

Job Description Job Purpose As a vital member of the Technology Group, you will contribute significantly to the development and delivery of the ICE Digital Trade Platform (IDT) - a cloud-based system designed to digitise, automate, and streamline global trade and post-trade processes. This platform manages the full lifecycle of digital trade documents across various applications, including API services. In this role, you'll be responsible for ensuring the quality and reliability of the IDT platform through test automation, certification testing, client onboarding, production support, and effective communication with stakeholders. We're looking for a results-oriented, self-driven professional who thrives in a dynamic environment. You'll collaborate closely with project managers, developers, product teams, support staff, and external vendors. Demonstrates awareness of emerging AI-driven QA methodologies and proactively explores opportunities to integrate them into existing testing frameworks and workflows to enhance efficiency, coverage, and quality assurance outcomes. Responsibilities Analyse business/system requirements and database schemas. Design, develop, and maintain automated test cases. Perform functional, integration, and regression testing (especially APIs). Write and execute SQL queries for data validation. Use CI/CD tools, version control (Git), and automation frameworks. Respond to client API queries and support onboarding. Log and track defects; coordinate client-side testing. Contribute to internal monitoring tools and scripting. Maintain awareness of environment changes and deploy applications. Collaborate with QA leads on complex testing strategies. Develop and deliver well written test plans and summaries Leverage AI coding assistants (e.g., Claude / Claude Code, GitHub Copilot) and agentic AI workflows to accelerate test authoring, maintenance, failure triage, and defect analysis, while reviewing AI-generated output for correctness and quality. Knowledge and Experience Expert QA experience, including test planning and automation. Expert experience with JavaScript or TypeScript and Cypress for front-end testing. Strong knowledge of REST/SOAP APIs, JSON, XML. Proficient in tools like JIRA, Postman, ReadyAPI, or JMeter. Experience with SDLC, QA methodologies, and scripting (Python, etc.). Excellent communication and organizational skills. Experience with Git, Jenkins, ALM, and test automation frameworks. Ability to work in highly demanding, fast-paced environment Self-motivated, able to work and excel autonomously on job responsibilities. Bachelor's degree in Computer Science, Engineering, or related field / similar experience. Hands-on experience using AI coding assistants such as Claude (including Claude Code) and GitHub Copilot to generate, refactor, and maintain automated test code, with sound judgement on where AI assistance is and is not appropriate. Working knowledge of agentic AI concepts - autonomous, multi-step AI agents and prompt engineering - applied to QA tasks such as test generation, log and failure analysis, and exploratory testing. Nice to Have Hands on experience designing and executing performance tests for APIs and GUIs to identify bottlenecks and ensure optimal response times Understands and evaluates AI/ML-based testing tools and frameworks, integrating them into CI/CD pipelines to optimise test automation and defect detection.

A leading technology firm in Stretford seeks a Senior AI Engineer to develop real-world AI systems that impact customer journeys and operations. The role involves designing and integrating AI applications, collaborating with various teams, and establishing best practices for deployment. Strong Python engineering skills and hands-on AI/ML experience are essential, along with a passion for applied AI. The position offers a hybrid working model and a culture focused on collaboration and growth.

We are looking for an Application Engineer (Multiphysics Simulation) to be the technical bridge between our customers and Allsolve, Quanscient's cloud-native simulation platform. You will work through engineering challenges across structural, thermal, acoustics, electromagnetics, and MEMS and show how they can be solved faster and at greater scale. In brief Location: Remote / Tampere / Munich / Glasgow Type: Full time, permanent Domain: Multiphysics simulation, deep tech Salary: €/month depending on experience Website: About Quanscient Quanscient is a deep tech company founded in 2021 and based in Tampere. Their cloud native simulation platform, Allsolve, lets engineering teams run multiphysics simulations across structural, thermal, acoustic, electromagnetic, and MEMS domains, faster and at far greater scale than legacy desktop tools allow. The platform is Python native, built for cloud, and increasingly AI integrated. Current customers include leading industrial companies across Europe, North America, and Japan. The team is 40 people from 15 nationalities, working from 17 cities. The role You will be the technical face of Allsolve, working directly with customers to work through their engineering problems and show how the platform handles them. The work spans the full customer journey, from first evaluation through to onboarding, productive use, and ongoing support. Beyond the direct customer work, you feed what you learn back into the product. You act as the link between the field and the Solver, ML, and Cloud Infrastructure teams, and you help grow the platform's reach through content, events, and pre sales support. This is a role for an engineer who enjoys both deep simulation work and working directly with people. What you'll do Build, run, and validate multiphysics simulations across structural/mechanical, thermal, acoustics and ultrasound, electromagnetics/RF, and piezoelectric/MEMS domains Lead technical evaluations and benchmarking studies against analytical models, published literature, and tools such as ANSYS, COMSOL, and Abaqus Onboard and train new users; deliver demos, webinars, and hands on workshops that get customers productive quickly Be the first point of contact for modelling questions, troubleshooting, and best practice guidance Develop simulation workflows in the Python SDK, run parameter sweeps and design optimisation, and build AI driven and agentic workflows alongside surrogate datasets Support Sales as the technical lead in pre sales evaluations, scoping customer problems and articulating how the platform delivers value Act as the voice of the customer toward the Solver, ML, and Cloud Infrastructure teams, translating field learnings into prioritised product feedback Produce application notes, case studies, and reusable example models Represent Quanscient at industry events, trade shows, and technical conferences, presenting applied results to engineers and decision makers What we are looking for The role sits at the intersection of deep simulation expertise and customer facing work. You need to be technically credible enough to earn the trust of engineers evaluating the platform, and clear enough in your communication to make complex physics feel approachable. Must have: A degree in engineering, physics, or a related field (BSc/MSc; PhD a plus), or equivalent hands on industrial experience Demonstrated experience with commercial simulation tools such as ANSYS, COMSOL, or Abaqus Solid understanding of FEM/FEA and the underlying physics of at least one application domain Comfortable scripting in Python to automate and scale simulation workflows Strong customer facing communication skills, able to explain complex simulation concepts clearly and translate between an engineering problem and the software that solves it Nice to have: Domain depth in MEMS, RF/electromagnetics, ultrasound/acoustics/piezoelectrics, or electric motors/superconductors Prior experience as an Application/Field Application Engineer, Solutions Engineer, or in technical pre sales Industry background in semiconductor, MedTech, automotive, aerospace, consumer electronics, or industrial R&D Familiarity with HPC or cloud simulation workflows Why Quanscient Quanscient is a team of 40 people from 15 nationalities, working from 17 cities. The founders brought together an unusual mix of expertise: world class simulation algorithms developed over nearly a decade of academic R&D, deep cloud infrastructure experience, and quantum computing research. That foundation is still visible in how the company operates, technically serious, with a low tolerance for nonsense and a genuine respect for hard problems. The culture is built around the idea that doing good work and enjoying the place you work are not in conflict. The team is distributed and remote work is standard, with people trusted to manage their own time. Decisions get made through open communication rather than hierarchy. The team is international by design, not by accident, and the expectation is that diverse backgrounds make the work better. If you want to work from an office, there are hubs in Tampere, Munich, and Glasgow.

Overview We are seeking to recruit two Senior Machine Learning Scientists to join our team on 18-month fixed-term contracts. These are hybrid roles, offering a combination of at home and on-site working. The role will support the development of a privacy-preserving generative AI ecosystem designed to protect personal and sensitive corporate data by design, including data residency, anonymisation and secure deployment protocols and principles, as well as model-based data protection achieved during both training and inference time. Working within a multidisciplinary and multi-partner environment, the postholder will research technical concepts core to the program and contribute towards developing them into dependable systems and components that can be used in live settings, while also supporting the broader technical development of the Lab and its collaboration with CODAS and other project partners. Additionally, the postholder will create publishable material based on the work carried out in the program, and attend events such as conferences and workshops to disseminate the progress made. Responsibilities Lead or substantially contribute to technical research workstreams within assigned CODAS tracks, setting experimental direction and ensuring delivery of high-quality outputs within programme timelines. Design, develop and validate machine learning models, algorithms and approaches for privacy-preserving generative AI, including areas such as differential privacy, federated learning, and secure inference. Serve as a technical research lead on assigned workstreams; design novel approaches or adapt existing methods to meet programme objectives beyond the current state of the art. Contribute to the translation of research outcomes into proof-of-concept systems, toolkits and deployable components that form part of the programme deliverables. Work with internal colleagues and external project partners to understand technical requirements, constraints and delivery priorities. Mentor and support junior colleagues (ML engineers and research associates), scoping and delegating tasks aligned to their development and the needs of the programme. Contribute to scientific publications, technical reports, conference presentations and other dissemination activities in support of the programmes commitment to advancing the field. Champion best practices in ML research, software design, experimental rigor, reproducibility and responsible AI across the team. Communicate complex technical findings and trade-offs clearly to both technical and non-technical stakeholders, including partner organisations and programme leadership. Knowledge, Experience and Qualifications - Essential A Master's degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a closely related discipline, or equivalent research or industry experience. 5+ year work experience in relevant field. A strong and demonstrable track record in applied ML/AI research or development, with recognised expertise in one or more relevant areas such as privacy-preserving ML, generative AI, large language models, or federated learning. Deep understanding of modern ML methods, their assumptions and limitations, and the ability to reason about appropriate application in novel or constrained settings. Strong software engineering foundations in Python, including ML frameworks, version control, CI/CD, and reproducible experimental practices. Strong skills handling challenging data that requires understanding, preparation, organisations and augmentation before use for the purposes of ML/AI research or development Experience working across the full ML lifecycle - from problem framing, experimentation and prototyping through to evaluation, integration and deployment. Ability to contribute to scientific publications, technical reports, and the dissemination of research findings to a wide audience. Proven ability to mentor or support junior colleagues and scope technical work aligned to team and programme goals. Strong communication skills, with the ability to explain complex technical concepts and trade-offs clearly to both technical and non-technical audiences. Ability to work effectively in technically complex and ambiguous environments involving multiple stakeholders. Knowledge, Experience and Qualifications - Desirable Experience with privacy-preserving ML techniques such as differential privacy, federated learning, secure computation, or membership inference resistance. Experience with generative AI systems, large language models, agentic AI approaches, or evaluation methodologies for frontier AI. Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps tooling. Contributions to open-source AI or ML projects, or a track record of published research in relevant areas. Experience working in a multi-partner programme involving industry, government or academia. Experience in consulting, client-facing AI delivery, or regulated industry contexts such as insurance, financial services, or public-sector programmes. Familiarity with agentic systems design, MCP/A2A protocols, or inference-time privacy controls. Familiarity innovating with agentic-driven workflows and toolsets for both research and prototyping tasks, such as Claude, Claude-Code, CoPilot, etc. Why Join Us? We're committed to your growth, providing the support to excel in your current role whilst offering opportunities to step into new challenges and drive your career forward. We realise that we need to be a good fit for you above all else - so here's what you can enjoy about AND-E: Recognised as the Best Large Insurance Employer: We are proud to have been named the Best Large Insurance Employer for 2023 at the prestigious British Insurance Awards. Unmatched Work-Life Balance. Competitive Salaries and Benefits Package: We offer competitive salaries that recognise your skills and expertise. We champion choice, flexibility, and balance in both work and home life. Our commitment to diversity, equity, and inclusion ensures everyone feels valued and supported - including embracing neurodiversity and providing the tools needed to thrive. We like to think our benefits package is one of the best, focusing on colleagues' health, wealth, and lifestyle. We offer: 28 Days annual leave with the option to buy/sell up to 5 days holiday Private Medical & Permanent Health Insurance 4 x Annual salary Life Assurance Health and Wellbeing Benefits: Including money back on health-related expenses (optician, dental, physio, etc.), Flu Jab vouchers, Virtual GP service, Employee Assistance Programme, and enhanced family-friendly policies (baby bonus & pension advisory service) Financial and Lifestyle Support: Offers financial flexibility through Wagestream , annual season ticket loans , cycle scheme , and £250 towards driving lessons for you and dependents . Subject to company performance and completion of probation Aioi Nissay Dowa Europe is committed to promoting equal opportunities in employment. Employees and job applicants will receive equal treatment regardless of age, disability, gender reassignment, marital or civil partner status, pregnancy or maternity, race, colour, nationality, ethnic or national origin, religion or belief, sex or sexual orientation (Protected Characteristics). Reasonable adjustments: If you require any adjustments to support you during our recruitment process, please let us know. We're committed to making the process accessible and are happy to help.

We're looking for a hands-on AI engineer ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies. As an Applied AI ML Lead - Vice President at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market leading AI products in a secure, stable, and scalable way. You will translate business problems into agentic AI and machine learning solutions, taking models from concept through to production grade services with measurable client and advisor impact. You will be responsible to the Head of AIML in IPB Tech for the end to end design, build, and production delivery of priority IPB AI/ML use cases, with particular focus on agentic AI applications, generative AI guardrails, and production ML supporting advisor and client journeys. Job responsibilities Owns end-to-end delivery of priority IPB AI/ML use cases, from problem framing and business case through to deployed, monitored production services with measurable advisor and client impact Leads the engineering build of agentic AI and LLM powered products serving IPB advisors and clients across the globe. Sets the engineering quality bar for the team's AI products through code reviews, technical design, and pairing with peers and junior engineers Establishes and operates Responsible AI controls in production (guardrails, evaluation frameworks, observability, and model risk controls) to firm wide standards Acts as a primary technical partner to IPB business stakeholders, surfacing new AI/ML opportunities and shaping them into funded workstreams Represents the AIML team in firm wide AI/ML governance and engineering forums; ensures cross-border, regulatory, and data privacy considerations are reflected in solution design Contributes to the team's GenAI education programme through training content, knowledge sharing sessions, and mentoring of junior engineers and interns Champions the firm's culture of diversity, Opportunity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification in software engineering concepts and expert applied experience Advanced proficiency in Python and modern software engineering practices (testing, design patterns, code review, version control) Fluent with AI coding tools (e.g., Claude Code, GitHub Copilot) as a core part of day to day software development, with the judgement to know when to lean on them and when not to Hands on experience building, evaluating, and deploying machine learning models into production Practical experience with Large Language Models, including prompt engineering, RAG, fine tuning, agentic frameworks, skills. Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data intensive systems Strong communication skills with confidence engaging senior business stakeholders and translating technical concepts for non technical audiences Experience applying new methods to determine solutions for complex technology problems across multiple technical disciplines MSc in Computer Science, Data Science, Engineering, or a related quantitative field Preferred qualifications, capabilities, and skills Postgraduate level qualification in data science, artificial intelligence, or machine learning Practical experience with CI/CD, containerization, and cloud native deployment patterns Experience within financial services technology, particularly wealth, private banking, or asset management Experience with Databricks, Kubernetes, or comparable ML / cloud platforms Experience designing or contributing to AI governance, model validation, or guardrail frameworks

Permanent contract UK Wide (London, Edinburgh, Manchester, Leeds, Birmingham) Talan Data x AI is a leading Data Management and Analytics consultancy, working closely with leading software vendors and top industry experts across a range of sectors, unlocking value and insight from their data. At Talan Data x AI, innovation is at the heart of our client offerings, and we help companies to further improve their efficiency with modern processes and technologies, such as Machine Learning (ML) and Artificial Intelligence (AI). Job Description As our AI practice grows, we are looking for a capable, hands on AI Engineer to help build and deliver the solutions at the heart of our client work. Coming from a data science background, you are equally comfortable applying proven machine learning techniques to traditional analytics problems and working with the latest generative AI (large language models, retrieval augmented generation, and agent based applications). You enjoy building, you write dependable code, and you turn well defined designs into working software. This is a delivery focused role that sits a level below our Lead Data Scientists. You will own meaningful workstreams within a project rather than the whole engagement, working to an architecture and plan shaped by more senior colleagues. In return, you will gain breadth across modern AI, exposure to real client problems, You will also be a supportive presence within the team, helping your peers solve problems, sharing what you learn, and giving day to day guidance to graduate and junior resources working alongside you. Required skills & experience: Build, test, and deploy AI and machine learning components within client projects to the standards and designs set by senior colleagues. Develop GenAI and LLM based features (prompting, retrieval augmented generation, and simple agentic workflows) alongside more traditional ML models. Deliver classical data science tasks (data preparation, feature engineering, model training, and evaluation) on projects that call for them. Translate solution designs into clean, well documented, production minded code. Contribute to model evaluation, testing, and monitoring. Support and guide very junior and graduate resources, reviewing their work and helping them grow. Help peers unblock technical problems and share knowledge across the team. Take part in client and team discussions and communicate your work clearly. Qualifications To qualify for this role, you must have: Typically, 2-4 years' experience in data science, machine learning, or AI engineering. Solid grounding in core data science and machine learning. Practical experience with LLMs and generative AI, including prompting and RAG. Familiarity with how AI agents are built and orchestrated. Strong Python and common data/ML libraries; good engineering habits. Clear communicator and collaborative team player. Ideally, you'll also have, but not essential: Exposure to cloud AI platforms, Azure a plus. Experience practicing MLOps and LLMOps. Early experience mentoring junior colleagues. Interest in consulting and client facing delivery. You must be: Willing to work on client sites, potentially for extended periods. Willing to travel for work purposes and be happy to stay away from homefor extended periods. Eligible to work in the UK without restriction. Additional Information Compensation & Benefits Package: BDP Plus - A reward programme whereby you accrue points to trade against a 3 month paid sabbatical or cash equivalent. 25 days holiday + bank holidays. 5 days holiday buy/sell option. Private medical insurance. Life cover. Cycle to work scheme. Eligibility for company pension scheme (5% employer contribution, salary sacrifice option). Employee assistance programme. Bespoke online learning via Udemy for Business. This is a role built for momentum, working across the full spectrum of modern AI, learning from senior architects and leads on real engagements, with genuine responsibility from day one and a clear path into a leadership track.

Job Description As ahands onAI Engineer, you will be at the heart of designing and building the components that make up advanced AI systems powering the modern enterprise. This is a deeply technical, hands on engineering role - you will spend the majority of your time in the detailed design, development, integration, and testing of AI system components across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements. You will take detailed architecture and design specifications and translate them into working, production quality software components. This means writing clean, well structured code, making low level design decisions within your assigned scope, and ensuring your components integrate reliably within the broader AI system. You will build and wire together the constituent parts of AI agent systems - including individual agent logic, tool integrations, skills, and memory components - and contribute to the development and integration of foundation and classical ML models into end to end pipelines. A hands on curiosity for the open source ecosystem is essential in this role. You will continuously evaluate, learn, and adopt relevant open source libraries and frameworks - such as those spanning agent orchestration, vector storage, model serving, and ML pipelines - selecting and applying the right ones for the problem at hand. Equally, you will configure, integrate, and operationalize third party AI technologies and platform services, understanding their capabilities and constraints deeply enough to make them work reliably within the context of a larger enterprise system. You will engineer components with enterprise grade qualities in mind, ensuring your work meets defined requirements across security, observability, governance, performance, and scalability. You will write and maintain the technical artifacts that accompany your engineering work - including low level design documents, component specifications, and integration contracts - ensuring your work is well documented, testable, and handoff ready. You will operate as a practitioner within cross functional delivery teams alongside data engineers, ML engineers, and application developers, taking direction from lead and principal architects while contributing meaningfully to technical problem solving and design discussions within your domain. This role is an opportunity to build deep, hands on expertise across the AI engineering stack, develop strong software engineering fundamentals applied to cutting edge AI systems, and grow toward a lead engineer or architect role over time. The Work Design, build, and configure individual agents - including their prompts, tools, and skills - and integrate them into multi-agent workflows Implement agent orchestration logic that handles task handoffs, communication, and error recovery Build evaluation harnesses and test suites that measure agent and component quality on metrics such as accuracy, relevance, and faithfulness, and share findings to inform design improvements Integrate foundation models into applications, selecting the appropriate model and invocation pattern for each use case Build and run model fine tuning pipelines - including data preparation and training - to adapt models to specific business domains, applying working knowledge of transformer based architectures Build ingestion pipelines that parse, chunk, enrich, and index unstructured enterprise content for retrieval Implement embedding generation, integrate vector databases, and develop retrieval components, including connectors and adapters that process unstructured content into end to end RAG pipelines Build the logic that assembles prompts and manages what information is passed to the model within its context window Implement memory components that store and recall conversational history and other relevant context Implement input/output guardrails, content filtering, and defenses against prompt injection Build PII detection and redaction components and integrate access controls for model and tool access Implement versioning, audit logging, and lineage tracking, and maintain model documentation that keeps the system auditable Instrument components with logging and tracing for requests, responses, token usage, and tool calls Contribute to monitoring, alerting, and cost tracking that keep AI systems healthy in production Continuously learn and apply new design patterns, technologies, and frameworks across the fast evolving AI landscape, bringing fresh approaches to the components you build Collaborate within cross functional teams to clarify requirements and ensure your components meet stakeholder needs Create and maintain clear technical documentation for the components you build, supporting troubleshooting and future development Qualification Education Bachelor's Degree in Computer Science, Computer Engineering, Data Science, or a related engineering discipline Basic (Required) Qualifications Experience (work or coursework) in designing, coding, building advanced AI solutions using agentic, generative and classical AI/ML using at least one cloud vendor. Experience (work or coursework) in the Agentic, LLM and Generative AI space. Experience (work or coursework) architecting and operationalizing LLM driven application architecture patterns. Experience in coding and engineering, machine learning, deep learning and NLP solutions and applications. Coding experience using Python Locations London Berlin Madrid Paris Additional Information All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. Accenture is committed to providing veteran employment opportunities to our service men and women. 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# Senior Principle Agentic AI OrchestratorOnsite Senior Level Full TimePosted: Yesterday Company LocationUnited Kingdom Remote Work PolicyOnsite SkillsPythonSQLNext.jsRESTSystem DesignEvent-Driven ArchitectureAWSAzureGCPRAGPrompt EngineeringAI AgentsGuardrailsLLMSalesforceServiceNow Be the one building AI-powered experiences where they matter most. At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships. Help build, support and operate technology used by more than 8,000 organizations in over 100 countries - moving AI from possibility to production in real-world enterprise environments every day. Agentic AI Orchestrator Professional Services AI & Digital Solutions EMEAAs an Agentic AI Orchestrator at Genesys, you will serve as the strategic and technical bridge between customer ambition and successful AI transformation.You will partner directly with strategic customers across the EMEA region - leading with a consultative approach, moving with the agility that enterprise AI demands, and measuring success against customer business outcomes. At Genesys, we're transforming how organisations connect with their customers through empathy, collaboration, and innovation.This role offers the opportunity to make a lasting impact by helping enterprises move from AI exploration to continuous transformation - and to shape how that transformation capability is built and scaled within the organisation. What You'll Do Advise, Influence & Drive Adoption Lead discovery and strategy alignment - partner with Genesys CX Advisors, Solution Consultants, and customer stakeholders to identify AI use cases, assess feasibility and value, and translate business KPIs into actionable technical priorities Surface the real problem beneath the presenting symptom - use data, evaluation outputs, and systematic analysis to form precise hypotheses and direct where intervention has the most leverage Influence adoption through credibility: when customers are blocked, uncertain, or sceptical, resolve it through evidence and clearly articulated reasoning Translate data-driven findings into executive-ready narratives - present complex AI performance insights, business impact, and recommendations with the clarity and confidence that influences C-level decisions Design and Architecture Define reference architectures, integration patterns, and data flows for AI-powered experience orchestration - adapting the approach iteratively as customer context and data reveals new priorities Lead process-redesign workshops to create seamless, channel-agnostic CX - facilitated with a consultative approach that builds customer ownership of the solution Ensure all designs comply with Genesys and customer security, privacy, and regulatory requirements (GDPR, PDPA, PCI, HIPAA where applicable) Prototype and Implementation Deliver rapid POC and MVP implementations using Genesys Cloud AI Studio, CoPilot, Agentic Virtual Agent, and related product suites - moving from hypothesis to working prototype at pace, and adjusting direction when the evidence requires it Integrate Genesys AI components with customer CRM, ERP, and third-party systems Establish implementation KPIs and analytics to measure model and journey performance from day one - not as an afterthought AI Engineering & Outcome-Oriented Delivery Design and implement evaluation frameworks to measure AI solution quality in production: intent accuracy, retrieval groundedness, response relevance, agent goal completion, and policy adherence Build automated eval pipelines that enable rapid, systematic iteration across prompt variants, guardrail configurations, and model versions Architect agentic systems with precision: define agent topology, design tool schemas, and engineer orchestration logic. Measure success through production adoption and demonstrable outcome improvement - use outcome data as the primary signal for where to focus next Optimisation and Continuous Improvement Define baseline metrics at engagement start and iterate relentlessly Evaluate solution performance against KPIs and refine designs based on data-driven insights, changing direction quickly when the data signals it Collaborate with Customer Success and Professional Services teams to hand over production-ready assets and roadmaps Codify field learnings into reusable frameworks, evaluation standards, and accelerators that scale capability beyond individual engagements Governance, Ethics, and Enablement Champion responsible AI design principles and apply guardrails to prevent bias or unsafe responses Adhere to Genesys ethical standards and compliance frameworks Mentor customer, partner, and internal teams to build long-term AI maturity and self-sufficiency - transferring expertise, not just delivering outcomes Feed well-formed, evidence-backed field signal to product and solution teams - precise enough to influence roadmap priorities directly What We're Looking For Experience Bachelor's degree (Master's preferred) in Computer Science, Information Technology, Data Science, or a related discipline 8-12 years of combined experience across AI implementation, CX/CCaaS platform consulting, or technical solution architecture - demonstrated through overlap and measurable customer impact, not additive year counts across separate tracks Extensive on-field experience implementing or supporting CX, CRM, or AI orchestration platforms (e.g., Genesys Cloud, Google CCAI, Salesforce, Microsoft, NICE CXone, AWS Connect, ServiceNow, or similar) Hands-on experience with agentic AI systems: building, evaluating, or operating LLM-powered agents in production contexts Demonstrated experience working with APIs, data pipelines, and modern cloud environments (AWS, Azure, GCP) Track record of mentoring or developing technical peers and codifying expertise into approaches others can build on Consultative & Customer-Facing Skills Proven autonomy inside complex enterprise accounts - leading discovery, shaping scope, managing stakeholder expectations, and building trust without hand-holding Comfortable operating without a defined playbook - able to form and test hypotheses under ambiguity, pivot quickly when the data signals a change, and bring stakeholders along through the process Translates data-driven findings into executive-ready narratives: quantified outcomes, causal relationships, and clear next steps - not qualitative summaries Proven leadership in cross-functional environments and complex enterprise contexts Product instinct: able to define success metrics, surface well-formed requirements, and articulate the business case for technical decisions Experience with industry verticals such as Financial Services, Healthcare, Insurance, Retail, or Public Sector Multilingual communication ability is an advantage across the EMEA region Technical Skills CX orchestration and workflow design across multiple platforms - with a focus on outcome over architecture elegance Conversational AI and Agentic Virtual Agent implementation across voice, chat, and messaging channels Knowledge engineering: retrieval system diagnosis, RAG pipeline design, semantic coverage analysis, and knowledge optimisation for AI consumption Agentic system design: tool schema authoring, multi-agent topology, prompt engineering as a systematic discipline, and guardrail implementation for enterprise-safe agent behaviour Applied data analysis: Python for evaluation scripting, log analysis, and integration development; SQL for operational data querying - AI-assisted development tooling expected and encouraged Deep working knowledge of one or more enterprise CCaaS or Agentic AI platforms - Genesys Cloud preferred, or equivalent such as Google CCAI, Salesforce, AWS Connect, NICE CXone, Sierra, Decagon, or Cognigy; demonstrated expertise in deploying and optimising conversational or agentic AI solutions on any of these platforms is equally valued Data and integration expertise: REST APIs, event-driven architecture, JSON Cloud infrastructure familiarity: provisioning, access control, and cost management (AWS preferred) Data governance, security compliance, and responsible AI design principles Working at Genesys AI at enterprise scale - Build, support and operate AI-powered technology used by more than 8,000 organizations worldwide. 150+ new AI features were released in the last fiscal year. A flexible-first culture - Join a global team of nearly 7,000 employees with flexible ways of working designed to help people do their best work. Growth in the AI era - Build future-ready skills through mentorship, learning programs, leadership development and education support. Time to recharge and give back - Benefits include paid volunteer time, August Free Fridays, well-being resources and regionally tailored programs for employees and their families. Recognized globally - Genesys is Great Place to Work certified in 17 countries and 94% of employees are proud to tell others they work at Genesys.Learn more about our culture, AI innovation and sustainability commitments through our Careers site and Sustainability Report. What Happens After You Apply After you apply, here's what you can typically expect: Our Talent Acquisition team reviews your application with the hiring team. A Talent Acquisition Partner will review your application and, if your background is aligned, schedule a Zoom interview. Next, you'll meet the hiring manager and other members of the interview team. We aim to keep the process focused and respectful of your time, with no more than five interviews in most cases. After interviews are complete, our team will follow up with the final steps.Every application is reviewed by a person . click apply for full job details

Trafigura is undergoing an exciting Digital Transformation, developing innovative AI technologies to change the way we work in Commodities Trading. The Document AI team is a key pillar of this initiative, unlocking data-rich proprietary documents to enable high-value process optimisation and data science use cases. We are seeking an Applied AI Engineer to grow our Document AI platform, developing robust and scalable AI solutions that solve real business problems. As a document-intensive industry, you will be building production ready Agentic AI/LLM systems that radically transform Commodities Trading. This is a hands on Individual Contributor role. Required Qualifications 5-8+ years of experience building production AI/ML systems Modern Python proficiency with deep knowledge of the ecosystem (Pydantic, FastAPI, asyncio, type safety) Experience with modern AI frameworks (we currently favour Pydantic AI) Production experience maintaining human in the loop systems and AI monitoring/observability Strong fundamentals in AI/ML evaluation frameworks Experience building agentic systems including MCP, tool calling, memory systems, vector-based knowledge stores, guardrails In depth understanding of modern software design principles (e,g. microservices, event-driven architectures, domain driven design, object oriented programming, test driven development) In depth understanding of modern software development lifecycle (CI/CD, IaC, Containerisation) Practical experience with cloud engineering (preference for AWS) Preferred Qualifications Prior experience in Commodities, Fixed Income, Equities, Asset Management is a plus Key Responsibilities Develop and maintain Python-based AI applications Build and maintain document workflows using LLMs and classical NLP Debug model performance issues, handle edge cases, and optimize system reliability Write comprehensive tests and implement monitoring for AI/ML systems Rapidly prototype new features, evaluate them, and implement to production Participate in code reviews, technical design discussions, and architecture decisions Communicate effectively with both technical and non-technical stakeholders to understand and translate business requirements into production code Example projects you might own in the first 6 months Develop a specialised agent to automate highly complex commodity trading workflow Develop an agent to operate within human workflows, including seamless UI integrations and long running durable executions Develop a new AI product to extract key insights from firmwide market intelligence emails for front and middle office Attributes for Success Engineering mindset focused on delivering practical solutions that solve real business problems Self-directed and comfortable working autonomously with minimal supervision Pragmatic approach to technology choices - you pick the right tool for the job rather than chasing trends Deep appreciation for AI system reliability - you understand that making AI systems work consistently is the hardest part of the job Intellectually curious and adaptable to rapidly evolving AI/ML landscape Strong desire to help people solve problems Key Relationships You will work closely with the Digital Transformation Team to understand business requirements and rollout production-grade applications to solve them. You will support the Data Science & Engineering teams as a Centre of Excellence for Agentic AI and related tech, providing support in the form of knowledge sharing and tool adoption. Reporting Structure You will report directly to the Document AI Lead. Equal Opportunity Employer We are an Equal Opportunity Employer and take pride in a diverse workforce. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, colour, religion, gender, sexual orientation, national origin, age, marital or veteran status, medical condition or handicap, disability, or any other legally protected status.

JOB DESCRIPTION Out of the successful launch of Chase in 2021, we're a new team, with a new mission. We're creating products that solve real world problems and put customers at the center - all in an environment that nurtures skills and helps you realize your potential. Our team is key to our success. We're people-first. We value collaboration, curiosity and commitment. As a Applied AI ML Lead at JPMorgan Chase within the Accelerator, you are the heart of this venture, focused on getting smart ideas into the hands of our customers. You have a curious mindset, thrive in collaborative squads, and are passionate about new technology. By your nature, you are also solution-oriented, commercially savvy and have a head for fintech. You thrive in working in tribes and squads that focus on specific products and projects - and depending on your strengths and interests, you'll have the opportunity to move between them. Job Responsibilities Design and develop scalable, self-service solutions for documentation, SDKs, configurations, and pipelines to enable rapid deployment of GenAI applications and agents Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor model performance and address data and model drift Recommend best practices for model integration and deployment patterns Design and implement effective testing strategies, including unit, component, integration, end-to-end, performance, and champion/challenger tests Ensure platform compliance with data privacy, security, and regulatory standards Mentor team members on platform design principles and best practices Guide colleagues on coding practices, design principles, and implementation patterns for high-quality, maintainable solutions Demonstrate proficiency in Java and/or Python programming languages Deploy production systems to GenAI platforms such as Google VertexAI, OpenAI, AWS Bedrock, or LangChain Utilize cloud technologies (AWS/Azure/GCP), distributed systems, CI/CD tools, infrastructure-as-code tools, and containerization/orchestration tools (Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred Qualifications, Capabilities and Skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Previous experience deploying and managing ML models Experience working in highly regulated environments or industries We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit ourFAQs for more information about requesting an accommodation.