Senior Machine Learning Engineer

Location(s): UK, Europe & Africa : UK :
London

BAE Systems Digital Intelligence is home to 4,500 digital, cyber and intelligence experts. We work collaboratively across 10 countries to collect, connect and understand complex data, so that governments, nation states, armed forces and commercial businesses can unlock digital advantage in the most demanding environments.

Job Title

Senior ML Engineer

Location

London. Full-time.

Job Description

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.

Core Duties

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

About you

You will have experience in many of the following:

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

It would be great if you also had experience in some of these, but if not we’ll help you with them:

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 Store)

Experience with containerisation (Docker) and orchestration (Kubernetes, ECS)

Familiarity with Infrastructure as Code (Terraform, Cloud Formation)

Experience with data processing frameworks (Spark, Dask) for large-scale workloads

Understanding of data governance and compliance frameworks

Experience working in regulated industries (finance,…