32 jobs found

Salary: £74,624 initial salary on confirmation of skills to achieve DDAT Group One Rate C. Additional payment of Recruitment and Retention Allowance of £6,251 on completion of a minimum term and successful achievement and validation of skills. Flexible working: we support full time, part time, compressed hours, job share and other flexible working patterns. Due to the sensitive nature of the role, remote, hybrid or home working is not possible. About us: We're MI6, also known as the Secret Intelligence Service (SIS). Our mission is to protect the security and economic wellbeing of the UK from overseas threats such as regional instability, terrorism, and cyber attacks. Working across the globe and in close partnership with MI5 and GCHQ, we help the Government to counter these threats through the provision of secret intelligence. A role in MI6 will see you providing vital support to this work, within a supportive and encouraging environment that puts the emphasis on teamwork. Responsibilities AI is being adopted across some of the most mission critical areas at MI6, creating a rare opportunity to influence how these technologies are secured from the start. As an AI Security Architect, you'll help shape how AI systems are designed, deployed and governed in environments where security, trust and resilience are essential. As part of a small, specialist team, you'll take responsibility for the technical security architecture that underpins AI solutions across the organisation. You'll define secure AI reference architectures, patterns and controls, and guide delivery teams on how these should be implemented in practice. Operating close to delivery, you'll influence design decisions early and ensure AI is built to be secure by default. The role sits at the intersection of established cyber security practice and rapidly evolving AI technologies. You'll assess the security implications of capabilities such as machine learning and large language models, considering risks related to model integrity, provenance, poisoning and misuse. Day to day, you'll work closely with software engineers, data scientists and ML engineers, reviewing technical designs and providing practical, actionable feedback. You'll also support assurance and accreditation colleagues, helping them understand complex architectures and clarify how security controls meet broader objectives. While most of your time is hands on and technical, you'll engage with senior leaders as needed to explain risks, trade offs and the value of proactive investment in AI security. This is a rare chance to help define how AI security is approached at organisational scale. As an individual contributor, you'll shape emerging practice, influence standards and ensure AI capabilities are introduced in a secure, responsible and sustainable way. Qualifications You're an experienced cyber or information security practitioner with a strong background in designing and assuring secure systems in complex environments. Your experience includes security architecture or senior security engineering roles, and you're comfortable taking responsibility for technical security decisions that influence how systems are built. You bring hands on experience working with AI enabled technologies and understand the security challenges they introduce. This may include exposure to machine learning, large language models or cloud based AI services, particularly within AWS environments. You're familiar with risks such as model integrity, provenance, poisoning and misuse, and understand how security controls must adapt as these technologies evolve. In an evolving technical landscape where precedent is limited and risks continue to emerge, your judgement and critical thinking are essential. You're able to operate where standards, controls and threat models are still developing, applying sound reasoning to make defensible security decisions. You're confident working directly with engineers, data scientists and ML practitioners, reviewing detailed technical designs and providing clear, actionable guidance on implementing secure patterns and controls. While deeply technical, you can also explain risk, trade offs and impacts in straightforward language, helping non specialist stakeholders make informed decisions. Above all, you're proactive, curious and motivated to stay ahead of emerging AI security challenges, valuing continuous learning and contributing specialist expertise within a small, collaborative team. Training and Development We're committed to supporting your ongoing development as a specialist security architect, recognising that AI and security are fast moving areas where continuous learning is essential. On joining, you'll receive an induction to the team, the organisation's AI landscape and the security contexts you'll be working within, helping you understand how your role supports wider objectives. You'll have protected time to stay current with emerging AI security threats, technologies and best practice. This includes access to a learning and development budget to support relevant certifications, training and professional development aligned to AI, cloud and security architecture. Development in this role is strongly practice led. You'll deepen your expertise through hands on work with delivery teams, reviewing live systems, shaping secure architectures and advising on complex technical decisions. Working within a small, experienced team provides regular opportunities for peer learning, technical challenge and shared problem solving. You'll also have opportunities to broaden your influence by engaging with assurance, architecture and leadership communities, helping bridge the gap between deep technical security considerations and wider organisational decision making. Development here focuses on sustaining technical excellence, staying ahead of change and building long term specialist capability in a rapidly evolving area. Rewards and Benefits You'll receive a starting salary of £74,624 plus other benefits including: 25 Days Annual Leave automatically rising to 30 days after 5 years' service, and an additional 10.5 days public and privilege holidays Opportunities to be recognised through our employee performance scheme Interest-free season ticket loan Cycle to work scheme Facilities such as a gym, restaurant and on-site coffee bars (at some locations) Paid parental and adoption leave Requirements The role is based in central London; candidates must reside within commuting distance. Candidates must be a British citizen or hold dual British nationality. This role requires the highest security clearance, Developed Vetting (DV). Equal Opportunities At MI6 diversity and inclusion are critical to our mission. To protect the UK, we need a truly diverse workforce that reflects the society we serve. This includes diversity in every sense of the word: those with different backgrounds, ages, ethnicities, gender identities, sexual orientations, ways of thinking and those with disabilities or neurodivergent conditions. We therefore welcome and encourage applications from everyone, including those from groups that are under-represented in our workforce such as women, those from an ethnic minority background, people with disabilities and those from low socio-economic backgrounds. Find out more about our culture, working environment and diversity on our website.

The impact you will have As Staff MLOps Engineer, you will define and build Elliptic's Enterprise MLOps platform. Elliptic has growing ML capability across several teams, an established model registry, and a maturing model risk management practice. What is missing is the unified platform layer that ties training, deployment, monitoring, and governance together into a coherent, scalable discipline. You will be responsible for creating that layer. Your platform will serve four distinct internal consumers, each with different needs: Product Engineering teams building customer-facing models and customer data analytical models, who need reproducible training pipelines, CI/CD for model deployment, and low-latency serving infrastructure Intelligence Research building frontier intelligence collection, predictive pre-screening models, and behavioural pattern detection, who need rapid experimentation, GPU orchestration, and dataset versioning InfoSec who own the model registry and model risk management framework today, and need the platform to close execution gaps in audit trails, drift monitoring, and compliance reporting Operations who own BI, usage prediction, and revenue opportunity signalling, and need scheduled batch inference, BI integration, and pipeline reliability The platform you build must enforce governance with enough rigour to satisfy a regulated financial crime context, while remaining flexible enough to avoid slowing down research teams who need to iterate quickly. This is a role for someone who has built ML infrastructure from the ground up before, who understands that a platform succeeds only when it is adopted, and who is comfortable making build vs buy decisions that others will adopt and use for years. What you will do Define the target state MLOps architecture for Elliptic, covering model training pipelines, serving infrastructure, monitoring, feature management, and governance, and produce the architecture decision records that inform investment decisions Make and document build vs buy vs stop recommendations with clear cost modelling and trade off analysis, evaluating vendors, open-source tools, and managed services against Elliptic's constraints (AWS primary, Databricks ecosystem) Work with InfoSec to improve the existing model registry and model risk management framework, closing identified gaps in metadata, lineage, approval workflows, and drift/bias detection Build model training pipelines, CI/CD for ML, and serving infrastructure, working directly with a small group of infrastructure engineers to ship production grade platform capabilities Instrument observability across the ML lifecycle: training metrics, serving latency and throughput, data quality, and prediction drift, integrating with Elliptic's existing observability stack Work directly with data scientists and ML engineers across all four consumer groups to onboard them onto the platform, writing documentation, runbooks, and reference architectures that lower the barrier to self service You will be a great fit here if you Have built MLOps platforms or ML infrastructure from the ground up, and can speak to what worked, what didn't, and why Have operated in a regulated industry (e.g. compliance, financial) and have hands on experience building ML infrastructure to meet those regulatory demands Think about ML infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced Are comfortable operating in ambiguity, making decisions with incomplete information, and creating structure where none exists, while remaining open to changing course when better information arrives Influence through clarity, evidence, and the quality of your work rather than positional authority. You earn adoption by making the platform genuinely better than the alternative Care about production engineering quality: you write production grade code, your systems are tested, observable, documented, and designed for others to operate Our ideal candidate has Deep hands on experience building MLOps platforms, including model registries, feature stores, and ML pipeline orchestration Working knowledge of model serving patterns: real time inference, batch prediction, A/B deployment, and deployment strategies AWS infrastructure experience (ECS/EKS, S3, IAM, networking) and comfort operating in a Databricks ecosystem or equivalent lakehouse architecture Experience with model monitoring: model evaluation, data drift detection, prediction drift, and performance degradation alerting A track record of building something from zero and bringing it to a state where others could operate and extend it Experience in a regulated industry (fintech, financial services, healthcare) where model governance is a compliance requirement See AI as a core part of how modern engineering gets done, not a passing trend. You actively use it to think faster, prototype faster, and pressure test your own designs, and you're excited that the bar keeps rising. Prior experience running formal build vs buy evaluations with written decision records Bonus Points for Familiarity with model risk management frameworks and the ability to connect governance practices to regulatory expectations Experience working simultaneously with research oriented ML teams and production oriented engineering teams, and understanding how their needs diverge Infrastructure as code fluency (Terraform) Experience with ClickHouse or similar OLAP engines for low latency ML feature serving Blockchain or crypto domain knowledge Experience working in fraud detection and modelling Contributions to open source MLOps tooling Job Benefits How we work Hybrid working and the option to work from almost anywhere for up to 90 days per year £500 Remote working budget to set up your home office space Learning & Development $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development Vacation/ Leave Holidays: 25 days of annual leave + bank holidays An extra day for your birthday Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully paid leave and leave. Benefits Private Health Insurance - we use Vitality! Full access to Spill Mental Health Support Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries £100 Crypto for you! Cycle to Work Scheme

The impact you will have As Staff MLOps Engineer, you will define and build Elliptic's Enterprise MLOps platform. Elliptic has growing ML capability across several teams, an established model registry, and a maturing model risk management practice. What is missing is the unified platform layer that ties training, deployment, monitoring, and governance together into a coherent, scalable discipline. You will be responsible for creating that layer. Your platform will serve four distinct internal consumers, each with different needs: Product Engineering teams building customer-facing models and customer data analytical models, who need reproducible training pipelines, CI/CD for model deployment, and low-latency serving infrastructure Intelligence Research building frontier intelligence collection, predictive pre-screening models, and behavioural pattern detection, who need rapid experimentation, GPU orchestration, and dataset versioning InfoSec who own the model registry and model risk management framework today, and need the platform to close execution gaps in audit trails, drift monitoring, and compliance reporting Operations who own BI, usage prediction, and revenue opportunity signalling, and need scheduled batch inference, BI integration, and pipeline reliability The platform you build must enforce governance with enough rigour to satisfy a regulated financial crime context, while remaining flexible enough to avoid slowing down research teams who need to iterate quickly. This is a role for someone who has built ML infrastructure from the ground up before, who understands that a platform succeeds only when it is adopted, and who is comfortable making build vs buy decisions that others will adopt and use for years. What you will do Define the target state MLOps architecture for Elliptic, covering model training pipelines, serving infrastructure, monitoring, feature management, and governance, and produce the architecture decision records that inform investment decisions Make and document build vs buy vs stop recommendations with clear cost modelling and trade off analysis, evaluating vendors, open-source tools, and managed services against Elliptic's constraints (AWS primary, Databricks ecosystem) Work with InfoSec to improve the existing model registry and model risk management framework, closing identified gaps in metadata, lineage, approval workflows, and drift/bias detection Build model training pipelines, CI/CD for ML, and serving infrastructure, working directly with a small group of infrastructure engineers to ship production grade platform capabilities Instrument observability across the ML lifecycle: training metrics, serving latency and throughput, data quality, and prediction drift, integrating with Elliptic's existing observability stack Work directly with data scientists and ML engineers across all four consumer groups to onboard them onto the platform, writing documentation, runbooks, and reference architectures that lower the barrier to self service You will be a great fit here if you Have built MLOps platforms or ML infrastructure from the ground up, and can speak to what worked, what didn't, and why Have operated in a regulated industry (e.g. compliance, financial) and have hands on experience building ML infrastructure to meet those regulatory demands Think about ML infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced Are comfortable operating in ambiguity, making decisions with incomplete information, and creating structure where none exists, while remaining open to changing course when better information arrives Influence through clarity, evidence, and the quality of your work rather than positional authority. You earn adoption by making the platform genuinely better than the alternative Care about production engineering quality: you write production grade code, your systems are tested, observable, documented, and designed for others to operate Our ideal candidate has Deep hands on experience building MLOps platforms, including model registries, feature stores, and ML pipeline orchestration Working knowledge of model serving patterns: real time inference, batch prediction, A/B deployment, and deployment strategies AWS infrastructure experience (ECS/EKS, S3, IAM, networking) and comfort operating in a Databricks ecosystem or equivalent lakehouse architecture Experience with model monitoring: model evaluation, data drift detection, prediction drift, and performance degradation alerting A track record of building something from zero and bringing it to a state where others could operate and extend it Experience in a regulated industry (fintech, financial services, healthcare) where model governance is a compliance requirement See AI as a core part of how modern engineering gets done, not a passing trend. You actively use it to think faster, prototype faster, and pressure test your own designs, and you're excited that the bar keeps rising. Prior experience running formal build vs buy evaluations with written decision records Bonus Points for Familiarity with model risk management frameworks and the ability to connect governance practices to regulatory expectations Experience working simultaneously with research oriented ML teams and production oriented engineering teams, and understanding how their needs diverge Infrastructure as code fluency (Terraform) Experience with ClickHouse or similar OLAP engines for low latency ML feature serving Blockchain or crypto domain knowledge Experience working in fraud detection and modelling Contributions to open source MLOps tooling Job Benefits How we work Hybrid working and the option to work from almost anywhere for up to 90 days per year £500 Remote working budget to set up your home office space Learning & Development $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development Vacation/ Leave Holidays: 25 days of annual leave + bank holidays An extra day for your birthday Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully paid leave and leave. Benefits Private Health Insurance - we use Vitality! Full access to Spill Mental Health Support Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries £100 Crypto for you! Cycle to Work Scheme

AI Developer (SC Cleared) £700-£800 per day Inside IR35 Hybrid (Canary Wharf, London - 1 day per week) Contract until September 2026 + potential extension Morgan Hunt are working in partnership with a leading UK government organisation to recruit an experienced AI Developer with active SC clearance.This is an exciting opportunity to play a key role in delivering cutting-edge AI solutions within a secure, high-impact environment-helping shape how AI is deployed across critical public services. The Role You'll be responsible for designing and delivering production-grade AI applications, working at the forefront of technologies such as Microsoft Copilot Studio, Azure AI Foundry and Azure OpenAI.Key responsibilities include: Designing, developing and deploying AI-enabled applications across cloud platforms Building and scaling Retrieval-Augmented Generation (RAG) pipelines and agentic AI systems using trusted data sources Creating, testing and refining prompts, workflows and AI configurations to maximise accuracy and performance Applying solid software engineering practices including API development, version control, CI/CD and automated testing Monitoring and optimising AI systems in production, ensuring performance, cost control, security and compliance Collaborating closely with Data Scientists to transition experimental models into robust, production-ready services Engaging stakeholders to identify and shape high-value AI use cases Ensuring all solutions align with Responsible AI principles, data protection and security standards Developing proof-of-concepts to support decision-making and future investment Experience Required Proven experience delivering AI-enabled solutions into production environments Strong software engineering background, particularly with Python and cloud technologies Experience working with large language models (LLMs) and AI-driven systems Track record of working within multidisciplinary agile teams Experience developing and advising on AI use cases and solution design Additional Details Day rate: £700-£800 (Inside IR35) Contract until end of September 2026, with likely extension Hybrid working - 1 day per week on-site in Canary Wharf Active SC clearance is essential (must have worked on a UK government project within the last 12 months) Morgan Hunt is a multi-award-winning recruitment business for interim, contract and temporary recruitment and acts as an Employment Agency in relation to permanent vacancies. Morgan Hunt is an equal opportunities employer. Job suitability is assessed on merit in accordance with the individual's skills, qualifications and abilities to perform the relevant duties required in a particular role.

AI Developer (SC Cleared) £700-£800 per day Inside IR35 Hybrid (Canary Wharf, London - 1 day per week) Contract until September 2026 + potential extension Morgan Hunt is working in partnership with a leading UK government organisation to recruit an experienced AI Developer with active SC clearance. This is an exciting opportunity to play a key role in delivering cutting edge AI solutions within a secure, high impact environment - helping shape how AI is deployed across critical public services. The Role You'll be responsible for designing and delivering production grade AI applications, working at the forefront of technologies such as Microsoft Copilot Studio, Azure AI Foundry and Azure OpenAI. Key responsibilities include: Designing, developing and deploying AI enabled applications across cloud platforms Building and scaling Retrieval Augmented Generation (RAG) pipelines and agentic AI systems using trusted data sources Creating, testing and refining prompts, workflows and AI configurations to maximise accuracy and performance Applying solid software engineering practices including API development, version control, CI/CD and automated testing Monitoring and optimising AI systems in production, ensuring performance, cost control, security and compliance Collaborating closely with Data Scientists to transition experimental models into robust, production ready services Engaging stakeholders to identify and shape high value AI use cases Ensuring all solutions align with Responsible AI principles, data protection and security standards Developing proof of concepts to support decision making and future investment Experience Required Proven experience delivering AI enabled solutions into production environments Strong software engineering background, particularly with Python and cloud technologies Experience working with large language models (LLMs) and AI driven systems Track record of working within multidisciplinary agile teams Experience developing and advising on AI use cases and solution design Additional Details Day rate: £700-£800 (Inside IR35) Contract until end of September 2026, with likely extension Hybrid working - 1 day per week on-site in Canary Wharf Active SC clearance is essential (must have worked on a UK government project within the last 12 months) Morgan Hunt is an equal opportunities employer. Job suitability is assessed on merit in accordance with the individual's skills, qualifications and abilities to perform the relevant duties required in a particular role.

Software Development Engineer, Advertising Job ID: Amazon Development Centre (London) Limited Orchestrating the selection of one out of tens of millions of ads, honoring advertiser targeting intent for hundreds of thousands of advertisers while ensuring great shopper experience for billions of shoppers millions of times per second on a latency of tens of milliseconds is not a trivial task. The demand retrieval team within the Amazon DSP organisation deals with this challenge, developing and operating machine learning models that match ads opportunities with the most relevant ads to deliver the right messages to the right customers at the right time. We are looking for a Software Engineer to optimize ad matching for Amazon's programmatic advertisement products. In this role you will work at the forefront of high scale low latency customer-facing advertising systems. You will bring best practices in MLOps and Software Engineering to process large datasets and enable the development foundation ML models in advertising. Your work will affect multi-billion dollar businesses, and you will be responsible for designing, testing and delivering significant breakthrough's for Amazon's business. You will embody engineering rigor, designing and executing proofs of concept that validate the technical efficacy and business value of our product hypotheses. Then bringing them into production, balancing speed of execution with operational excellence. Successful candidates will have strong technical ability, self driven, excellent teamwork, and communication skills, and a motivation to achieve business results in a fast-paced environment. Key job responsibilities Work side by side with our scientists to deliver code changes impacting our ads stack Rapidly prototype and test many possible hypotheses/implementation alternatives in a high ambiguity environment, making use of both quantitative analysis and business judgement Work with very large datasets. Integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Automate, monitor and self heal our systems A day in the life You will partner with our product teams, bringing your own ideas to the conversation and aligning on work, adjusting priorities based on business requirements and fast iteration on experiments. You will work with scientists on rapid data gathering prototypes and analyses and represent the rigor of engineering in the conversation; you will engage with scientists, product and finance to align on the benefits of the proposed approach. You will help harmonise our product stack, enabling simple monitoring and self healing that allow us to do more with less through automation and rapid diagnostic tools. About the team The Demand Retrieval team is responsible for designing, implementing, deploying and operating machine learning models that match bid opportunities to ads demand based on performance, campaign delivery, and targeting objectives specified by advertisers. We measure the success of our approaches based on offline experimentation and and online metrics that measure the impact of our matching models on campaign KPIs (e.g.: cost per action, return on ads investment, budgets delivered, and targeting precision). Basic Qualifications Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design Experience (non-internship) in professional software development Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems Preferred Qualifications Bachelor's degree in computer science or equivalent Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( ) to know more about how we collect, use and transfer the personal data of our candidates. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.