NSL- ML Engineer

Overview

In this Senior ML Engineer role, you design, develop and iterate ML models to support national security objectives, collaborating with data scientists, software engineers and government stakeholders. You balance rapid experimentation with production readiness, deploying scalable ML pipelines on AWS and integrating GenAI/LLM solutions into operational systems. You’ll contribute to meaningful AI adoption for national security and guide cross-functional teams toward measurable impact.

Pay / Benefits

• private medical and dental insurance
• pension scheme
• cycle to work scheme
• taste cards
• 25 days holiday
• bonus scheme

Responsibilities

• Design and develop ML models for traditional tasks and GenAI/LLM applications
• Lead experimentation cycles with hypotheses, experiments and governance
• Transition validated experiments to production-ready solutions with 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 monitoring
• Implement robust experiment tracking, model versioning and reproducibility with audit trails
• Design feature engineering approaches and contribute to feature store development
• Support production models through monitoring and continuous improvement
• Apply responsible AI practices including explainability and fairness assessment
• Present findings to stakeholders and articulate operational value
• Mentor junior colleagues and share learnings across the team

Key requirements

• Hands-on experience developing and deploying ML models in Python using scikit-learn, XGBoost, PyTorch or TensorFlow
• Strong experience with AWS ML services (SageMaker, Lambda, S3) in production
• Experiment design skills including hypothesis formulation, A/B testing and statistical evaluation
• Proven track record transitioning models from experimentation to production with governance and quality controls
• Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC)
• Experience developing LLM/GenAI applications including prompt engineering and RAG architectures
• Familiarity with LLMOps tools such as LangSmith, LangChain, or LangGraph
• Understanding of model evaluation, validation techniques, and production monitoring
• Experience working in cross-functional teams from problem framing to production delivery
• Ability to communicate complex findings to non-technical audiences
• Strong problem-solving and judgment about when AI is appropriate
• clear communication
• cross-functional collaboration
• problem-solving
• Python ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow)
• AWS ML services (SageMaker, Lambda, S3)
• Experiment design and governance

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