Machine Learning Engineer

Overview

In this role you will design, build and operationalise advanced ML and GenAI solutions that support UK national security objectives. You will work in a multidisciplinary AI engineering environment, bridging data science, software engineering and government stakeholders. From experimentation to production-grade deployment on a modern AWS-based MLOps platform, you will drive high-impact, scalable ML delivery and responsible AI practices across live systems. This position offers meaningful impact at scale within critical national operations.

Responsibilities

• Design, develop and optimize ML models across traditional use cases and GenAI/LLM solutions
• Lead experimentation cycles with governance and documentation
• Transition validated experiments into production ML services with deployment and monitoring
• Build scalable ML pipelines using AWS services and modern experiment tracking
• Develop and integrate LLM-powered capabilities for evaluation, tracing and monitoring
• Ensure robust experiment tracking, model versioning and reproducibility with auditability
• Design feature engineering strategies and contribute to feature store
• Monitor live models and drive continuous improvement
• Apply responsible AI principles (explainability, robustness, fairness)
• Communicate results to stakeholders highlighting operational value
• Mentor junior engineers and promote best practices

Key requirements

• Commercial experience developing and deploying ML models in Python
• Proficiency with ML frameworks (scikit-learn, XGBoost, PyTorch or TensorFlow)
• Strong experience delivering ML solutions on AWS (SageMaker, Lambda, S3)
• Expertise in experiment design (hypothesis, A/B testing, statistical evaluation)
• Proven experience moving models from experimentation to production with governance and quality controls
• Hands-on experience with MLOps tooling (MLflow, Weights & Biases, Data Version Control)
• Practical experience building LLM/GenAI applications (prompt engineering, RAG)
• Familiarity with LLMOps frameworks (LangChain, LangSmith, LangGraph)
• Understanding of model validation, evaluation techniques and production monitoring
• Experience in cross-functional delivery and clear technical communication
• Judgement in applying AI appropriately and knowing when non-AI approaches are better

• Communication skills
• Cross-functional collaboration
• Mentoring and leadership
• Python ML development
• ML frameworks: scikit-learn, XGBoost, PyTorch, TensorFlow
• AWS services: SageMaker, Lambda, S3

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