Senior Machine Learning Engineer

Job Title

Senior ML Engineer

London. Full-time.

Are you passionate about cutting‑edge AI and machine learning in digital services, and want to deliver positive real‑world value to the UK? We are looking for a ML Engineer to join our team and help solve a variety of interesting problems in the national security space. At BAE Systems Digital Intelligence, we work with a wide range of government customers, across defence, space, and government.

In our national security AI team, we undertake a variety of projects covering exploratory research into AI methods and approaches, bespoke solutions to complex customer problems, and infrastructure projects working across large customer datasets. As a Senior ML Engineer, you will design, develop, and iterate on machine learning models that support national security objectives. You will collaborate with Data Scientists, Software Engineers, Product Management and Government business stakeholders across the full lifecycle, from hypothesis through to production deployment.

Leveraging our AWS‑based infrastructure you will apply modern MLOps/LLMOps tooling to run rigorous experiments, track results, and deliver scalable solutions. A key aspect to the role is to balance rapid experimentation with production readiness, prototyping and validating ML approaches while ensuring successful experiments integrate seamlessly into operational systems. This is an exciting time to join our team to help pioneer both our customer’s and our own AI adoption journey.

Not only will you be directly making a huge impact through the solutions you develop, you’ll be doing it for an organisation who makes a huge impact to the security of the UK.

• Design and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applications
• Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements
• Transition validated experiments into production‑ready solutions, working closely with other engineers on deployment and monitoring
• Build and optimise ML pipelines using AWS services and experiment tracking tools
• Develop and integrate LLM‑powered solutions for tracing, evaluation, and production monitoring
• Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails
• Design feature engineering approaches and contribute to feature store development
• Support production models through monitoring, performance analysis, and continuous improvement
• Apply responsible AI practices, including model explainability and fairness assessment
• Present experiment findings and production outcomes to stakeholders, articulating operational and strategic value
• Mentor junior colleagues and share learnings across the team

• Hands‑on experience developing and deploying ML models in Python using frameworks such as scikit‑learn, XGBoost, PyTorch, or Tensor Flow
• Strong experience with AWS ML services (Sage Maker, Lambda, S3) in production environments
• Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation
• Proven track record transitioning models from experimentation to production with appropriate governance and quality controls
• Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control)
• Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures
• Familiarity with LLMOps tooling such as Lang Smith, Lang Chain, or Lang Graph
• Understanding of model evaluation, validation techniques, and production monitoring
• Experience working in cross‑functional teams from problem framing through to production delivery
• Ability to communicate complex findings to non‑technical audiences clearly
• Strong problem‑solving skills and knowing when AI is not the answer

• Experience with advanced LLM techniques: agents, tool use, and agentic workflows
• Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications
• Experience with feature stores (Feast, AWS Feature…