About the role

Senior ML Engineer designing and developing machine learning models for national security. Collaborating with cross-functional teams to deliver scalable solutions in defense applications.

Responsibilities

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 TensorFlow
Strong experience with AWS ML services (SageMaker, 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 LangSmith, LangChain, or LangGraph
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.

Work-life balance is important; you can work around core hours with flexible and part-time working
25 days holiday a year and the option to buy/sell and carry over from the year before
Our flexible benefits package includes private medical and dental insurance, a competitive pension scheme, cycle to work scheme, taste cards and more
You’ll have a dedicated Career Manager to help you develop your career and guide you on your journey through BAE
You’ll be part of our company bonus scheme
You are welcome to join any/all of our Diversity and Support groups.