Aventum Group

Role Summary We are seeking an exceptional Principal Software Engineer - AI Native to lead the design, development, and delivery of business-critical software platforms across Aventum Group. This is a hands-on leadership role for a highly experienced software engineer who combines deep technical expertise with an AI-first mindset. You will be responsible for architecting scalable solutions, driving engineering excellence, and leveraging AI-native development practices to accelerate software delivery and innovation across the business. As a Principal Engineer, you will act as a technical authority across multiple initiatives, influencing architecture, engineering standards, AI adoption, cloud strategy, and software delivery practices whilst remaining actively involved in solution design and development. Role Accountabilities Technical Leadership & Architecture Define and drive engineering standards, architecture principles, and software development best practices. Design scalable, resilient, and secure enterprise solutions across frontend, backend, data, and cloud environments. Provide technical leadership across multiple engineering teams and strategic initiatives. Lead architectural decision-making and technology selection processes. Champion modern engineering approaches including microservices, event-driven architectures, APIs, and distributed systems. Drive technical governance while maintaining delivery speed and engineering quality. Software Engineering Build and deliver high quality software soultions using: TypeScript, React, C #/.NET and cloud native technologies. Design and develop APIs, integrations, and platform services. Take ownership of solution delivery from specification through deployment and ongoing optimisation. Review code and ensure engineering quality, maintainability, security, and performance. Support complex troubleshooting, performance tuning, and root-cause analysis. AI-Native Engineering Lead adoption of AI-assisted development tools and agentic AI workflows. Apply Spec-Driven Development principles to improve software quality and delivery speed. Use AI to accelerate: Requirements gathering. Technical specifications. Coding Testing. Documentation. Deployment. Validate and assure the quality of AI-generated outputs. Drive productivity improvements through AI-enabled engineering practices. Cloud & DevOps Design and deliver cloud-native solutions primarily within Microsoft Azure. Improve CI/CD pipelines, deployment automation, and Infrastructure-as-Code capabilities. Implement secure, scalable, and observable cloud architectures. Drive best practice across monitoring, resilience, security, and operational excellence. Collaboration & Influence Partner with Product, Data, Technology, and Business stakeholders. Translate complex business requirements into scalable technical solutions. Mentor engineers and contribute to knowledge sharing across the engineering function. Influence the future direction of software engineering and AI adoption within Aventum. Any additional duties as assigned. Role Requirements Significant experience as a Senior, Lead, Staff, or Principal Software Engineer. Strong expertise in TypeScript, React, C#, and .NET. Proven experience building enterprise-scale applications and distributed systems. Deep understanding of software architecture, design patterns, testing, and engineering best practices. Experience building APIs, integrations, and microservices. Strong understanding of secure software development principles. Hands-on experience with Microsoft Azure, AWS, or GCP. Experience with: Docker, Kubernetes., CI/CD pipelines Infrastructure as Code (Terraform or equivalent) Strong understanding of cloud-native architecture and operational excellence. Strong database design and optimisation experience. Experience with relational and NoSQL databases. Understanding of complex data flows, integration architectures, and performance optimisation. Practical experience using AI-assisted engineering tools. Strong AI literacy and understanding of agentic AI workflows. Experience reviewing and validating AI-generated code and technicial outputs. Demonstrated use of AI to improve engineering productivity and delivery outcomes. Skills and Abilities Technical Excellence Enterprise architecture and solution design. Full-stack engineering expertise. Cloud-native development. Distributed systems and integration design. DevOps, CI/CD, and Infrastructure-as-Code. Database and data engineering principles. Leadership & Influence Strong stakeholder management skills. Ability to influence technical direction without direct authority. Excellent communication and presentation skills. Ability to simplify complex technical concepts for non-technical audiences. AI-First Mindset Passion for emerging technologies and AI innovation. Continuous improvement mentality. Ability to combine engineering judgement with AI-enabled productivity. Focus on delivering business value through technology. Management Duties Yes Benefits include Personal training sessions Additional holiday for length of service Private healthcare for you and your family Pension Discretionary annual bonus Enhanced maternity and paternity benefit Day off on your birthday Season ticket loan Cycle to work scheme Health and wellbeing support Discounts and rewards Death in service Employee share scheme

Role Summary We are seeking several hands-on Senior Software Engineers with strong full-stack expertise across TypeScript/React, C#/.NET and modern cloud-native technologies. You will design, build and deliver software solutions end-to-end, owning the development lifecycle from discovery and technical specification through implementation, testing, deployment and continuous improvement. As an AI-native engineer, you will use approved AI-assisted development tools and agentic AI workflows to accelerate delivery while maintaining high standards of quality, security and engineering excellence. You will work confidently with ambiguity, ask focused clarifying questions, compare alternative approaches and translate complex business needs into structured, testable solutions. This is a senior individual contributor role focused on software engineering, implementation and technical delivery rather than people or project management. Success will be measured through engineering impact, delivery outcomes and software quality. Key Responsibilities Design, build, test, deploy and support production-grade software solutions across the full technology stack. Develop scalable applications using TypeScript, React/Angular or another modern UI framework, C#/.NET and related technologies. Lead technical discovery by asking effective clarifying questions before moving into detailed solution design or implementation. Translate ambiguous or complex business requirements into clear, structured and testable technical specifications using Spec-Driven Development principles. Evaluate alternative technical approaches and clearly explain the benefits, risks, constraints and trade-offs. Break complex problems into manageable engineering tasks and deliver practical, proportionate solutions without unnecessary complexity. Write secure, maintainable and high-quality code that follows modern engineering standards and best practices. Design and build RESTful APIs, integrations, microservices and event-driven solutions focused on performance, reliability and scalability. Deliver solutions spanning frontend, backend and data layers using clean architectural principles. Use AI-assisted development tools and agentic AI workflows across requirements analysis, specification, coding, testing, debugging, documentation and delivery. Take ownership of reviewing, validating and assuring the quality, security and accuracy of both human-written and AI-generated outputs. Develop distributed systems and integrations that support complex workflows and data-processing requirements. Implement comprehensive automated testing, including unit, integration and regression testing. Troubleshoot technical issues using structured debugging and root-cause analysis techniques. Improve CI/CD pipelines, deployment automation, observability and Infrastructure as Code practices. Build cloud-native applications using compute, serverless, messaging, storage and identity services. Contribute to architectural discussions while remaining actively involved in hands-on coding and implementation. Collaborate with product owners, analysts, designers, testers and engineers to deliver effective business solutions. Produce clear technical documentation covering specifications, design decisions, operational support and known trade-offs. Demonstrate solutions through working software, prototypes, technical documentation and measurable delivery outcomes. Constructively challenge requirements and technical assumptions while remaining open to feedback and alternative approaches. Identify opportunities to improve engineering effectiveness through AI, automation, reusable components and repeatable patterns. Participate in code reviews, share knowledge and contribute to engineering best practice and continuous improvement. Undertake any other duties reasonably required. Essential Experience Software Engineering and Delivery Significant recent hands-on experience in software development and object-oriented programming. Proven experience owning complex software solutions from requirements discovery through design, development, testing, deployment and production support. Strong experience building scalable full-stack applications using TypeScript/React/Angular or another modern UI framework, and C#/.NET. Deep understanding of software engineering fundamentals, including design patterns, SOLID principles, modular architecture, clean code and testing practices. Demonstrable experience making pragmatic technical decisions and assessing alternative solution designs. Experience working with incomplete or ambiguous requirements and converting them into clear technical specifications. Proven experience developing RESTful APIs, integrations, microservices and event-driven systems. Experience delivering end-to-end solutions across frontend, backend and data platforms. Solid understanding of distributed systems, service communication patterns and complex data flows. Strong understanding of secure development practices and software testing methodologies. Experience delivering production-grade software within Agile environments. Cloud and DevOps Hands-on experience with cloud platforms, ideally Microsoft Azure, although relevant AWS or GCP experience will also be considered. Experience with cloud-native services including compute, serverless, messaging, storage and identity management. Familiarity with Docker and Kubernetes. Experience implementing modern deployment patterns and scalable cloud architectures. Experience building and maintaining CI/CD pipelines. Knowledge of Infrastructure as Code tools such as Terraform or equivalent. Strong understanding of monitoring, observability, cloud operations and production support. Data and Integration Strong understanding of database design, optimisation and data modelling. Experience working with relational and NoSQL databases. Knowledge of data-access patterns, performance optimisation and system-integration design. AI-Native Engineering Strong AI literacy with practical experience using AI-assisted development tools within software engineering environments. Practical experience applying Spec-Driven Development or a similar specification-led engineering approach. Evidence of using AI across requirements analysis, specification creation, coding, testing, debugging, documentation and code review. Experience designing or using agentic workflows involving multiple stages, tools or automated actions. Ability to critically review, test, secure and improve AI-generated outputs before production use. Examples of improvements in delivery speed, software quality, test coverage or developer productivity achieved through AI-assisted engineering. Ability to explain the problem addressed, personal contribution, tools used, validation approach and outcome for software delivered using AI-assisted or agentic workflows. Skills and Competencies Technical Excellence Deep full-stack engineering capability across frontend, backend, data and cloud-native technologies. Strong solution-design and architectural thinking, balancing business needs with security, maintainability, reliability and technical quality. Expertise in API development, integrations, microservices and distributed systems. Strong understanding of DevOps, CI/CD, Infrastructure as Code, deployment automation and observability. Solid foundation in database technologies and data-engineering principles. Ability to discuss implementation decisions at code, application and system-design level. AI-Native Mindset Uses AI tools and agentic workflows to improve productivity, quality and delivery outcomes, not solely to generate code. Applies sound engineering judgement, governance and validation when working with AI-generated outputs. Maintains accountability and control when using AI to accelerate software development. Converts complex requirements into clear, testable specifications that improve delivery speed and quality. Remains curious about emerging capabilities while selecting tools and approaches pragmatically. Professional Skills Asks focused, relevant questions before moving into detailed design or implementation. Demonstrates strong problem-solving and analytical ability. Responds constructively to challenge and uses feedback to strengthen technical decisions. Communicates technical trade-offs clearly to both technical and non-technical audiences. Balances delivery speed with security, maintainability, reliability and long-term value. Works effectively with ambiguity using a structured and evidence-led approach. Takes accountability for outcomes rather than relying solely on tools, frameworks or AI recommendations. Collaborates effectively with stakeholders and multidisciplinary delivery teams. Actively contributes to knowledge sharing, code reviews and team-wide engineering excellence. Candidate Evidence During the selection process, candidates should be prepared to provide practical examples that demonstrate: . click apply for full job details

Role Summary We are seeking an AI Native DevOps / Platform Engineer who combines strong cloud infrastructure and platform engineering expertise with hands-on experience delivering AI-enabled platforms, automation, and engineering solutions. You will design, automate, secure, and operate the Azure platforms that power our products, whilst developing cloud-native solutions using AI-first engineering practices. Working closely with Product Engineering Teams and Technical Leadership, you will build the cloud platforms, infrastructure, deployment pipelines, automation, observability frameworks, and engineering tooling that enable the rapid delivery of both AI-powered and traditional cloud-native applications. We are building an AI-native engineering capability where AI agents and intelligent automation are increasingly used to design, provision, deploy, operate, and optimise cloud platforms under appropriate governance and control. Role Accountabilities DevOps & Infrastructure Build and maintain Infrastructure as Code (IaC) using Terraform and similar technologies, leveraging AI-assisted and agent-driven engineering practices. Design resilient, secure, and highly available cloud architectures. Deliver cloud-native infrastructure solutions using Azure Functions, container platforms, and managed cloud services. Optimise platform performance, scalability, reliability, and cost. CI/CD & Developer Experience Build world-class deployment pipelines and automation that enable fast and reliable software delivery. Create self-service development environments and platform tooling. Drive engineering productivity through AI, automation, self-service capabilities and platform standardisation. Improve release processes and operational excellence across teams. Reliability & Observability Implement monitoring, logging, tracing, and alerting solutions. Establish platform SRE principles and operational standards. Proactively identify and resolve reliability, security, and performance issues. Lead incident response and continuous improvement initiatives. Security & Governance Embed security throughout the software delivery lifecycle. Implement security, governance, compliance, and operational controls across production environments. Champion secure AI deployment practices. AI Platform Engineering Build, operate, and continuously improve platforms that enable AI agents to provision, test, deploy, configure, and manage cloud solutions safely, securely, and at scale. Develop guardrails, governance controls, and approval workflows for AI-driven infrastructure and platform operations. Drive the use of AI technologies to improve engineering productivity, operational efficiency, platform reliability, and solution delivery. Create reusable AI platform services and automation capabilities. Support model serving infrastructure, and AI observability tooling. Role Requirements Significant experience as a Platform Engineer, DevOps Engineer, SRE, or Cloud Engineer. Strong expertise with Azure cloud technologies. Deep understanding of cloud-native platforms and container orchestration. Experience building CI/CD pipelines using Azure DevOps, or similar platforms (GitHub Actions, etc). Strong Infrastructure as Code experience using Terraform. Experience deploying distributed systems and microservices architectures. Strong understanding of cloud networking, security, and platform operations. Experience using AI to improve infrastructure delivery, engineering productivity, operational efficiency, and software delivery outcomes. Experience supporting modern software engineering teams in a fast-paced environment. Skills and Abilities Deliver highly scalable cloud infrastructure supporting our products. Establish best practices for AI-native platform engineering, agent-driven automation, and cloud infrastructure delivery. Improve platform reliability, resilience, and security posture. Drive platform automation, engineering efficiency, and operational excellence through AI-assisted and agent-driven approaches. Design secure operating models for AI-driven infrastructure provisioning and change management. Support rapid experimentation and innovation within an ambitious product led environment. Management Duties No Benefits include Personal training sessions Additional holiday for length of service Private healthcare for you and your family Pension Discretionary annual bonus Enhanced maternity and paternity benefit Day off on your birthday Season ticket loan Cycle to work scheme Health and wellbeing support Discounts and rewards Death in service Employee share scheme

Role Summary As an AI Engineer, you will design, implement, and productionise behavioural learning systems that integrate directly into our digital products and workflows. You will focus on turning advanced behavioural, sequential, and causal AI models into reliable, scalable, maintainable production systems that power Digital Twins, agentic decision engines, and intelligent automation across our digital suite. This role bridges model development and real world implementation. You will work hands on with software engineers, ML engineers, and product teams to ensure behavioural intelligence is embedded end to end from data pipelines and inference services through to live product decisioning and monitoring. Role Accountabilities Design, implement, and deploy AI models that predict, optimise, or automate decision-making within production digital workflows. Translate behavioural and sequential modelling approaches (e.g. sequence prediction, intent modelling, imitation learning) into robust, production-ready systems. Build and maintain end-to-end AI pipelines, including data ingestion, feature engineering, model training, inference, and monitoring. Apply causal inference techniques to evaluate the real-world impact of AI-driven decisions and support data-informed product changes. Integrate AI services into existing platforms via APIs, microservices, and event-driven architectures in close collaboration with engineering teams. Partner with product and platform teams to ensure AI outputs are actionable, explainable, and aligned with business workflows. Support Digital Twin and agentic systems by implementing behavioural dynamics, state modelling, and decision process representations. Validate deployed models using offline replay, A/B testing, shadow deployments, and simulation frameworks. Ensure solutions meet production standards for scalability, reliability, security, and observability. Contribute to engineering best practices around testing, versioning, CI/CD, and model lifecycle management. Role Requirements Strong foundation in machine learning, applied statistics, or software engineering with a focus on building production systems. Demonstrated experience implementing sequence- or decision-based models (e.g. LSTM, Transformers, Markov models, RL-inspired methods) in real applications. Practical experience deploying AI models into production environments, not just experimentation or notebooks. Familiarity with behavioural modelling concepts such as imitation learning, behavioural cloning, or decision process modelling. Working experience with causal inference tooling or methodologies (e.g. DoWhy, EconML, CausalML) applied to real data. Strong Python skills and experience with ML frameworks such as PyTorch or TensorFlow. Experience building or integrating AI systems using APIs, services, pipelines, or orchestration frameworks. Comfortable collaborating in engineering-led product environments, balancing research insight with delivery constraints. Strong problem-solving skills, with the ability to iterate quickly from prototype to production. Skills and Abilities Strong expertise in machine learning, behavioural modelling, and AI-driven decision systems. Experience building and deploying production-ready AI solutions using Python, PyTorch, TensorFlow, or similar frameworks. Strong understanding of sequence modelling, intent prediction, causal inference, and agentic AI concepts. Ability to build scalable end-to-end AI pipelines, APIs, and microservices. Experience working with cloud-native architectures, CI/CD pipelines, monitoring, and observability practices. Strong analytical and problem-solving skills with the ability to translate research into business value. Excellent collaboration and communication skills, working effectively across engineering, product, and business teams. Self-motivated with a continuous improvement mindset and passion for emerging AI technologies. Management Duties No Benefits include Personal training sessions Additional holiday for length of service Private healthcare for you and your family Pension Discretionary annual bonus Enhanced maternity and paternity benefit Day off on your birthday Season ticket loan Cycle to work scheme Health and wellbeing support Discounts and rewards Death in service Employee share scheme