Senior Machine Learning Engineer

We’re working with a leading technology consultancy delivering cutting-edge AI solutions within the UK National Security sector. They’re looking for a Senior Machine Learning Engineer to design, develop, and deploy production-ready machine learning solutions that make a genuine impact across defence, government, and national security.

Working alongside Data Scientists, Software Engineers, and AI specialists, you’ll take projects from experimentation through to production, leveraging modern MLOps and LLMOps practices on AWS to build scalable, secure AI systems.

What You’ll Be Doing

• Design, develop, and deploy machine learning models for traditional ML use cases, including forecasting, classification, and anomaly detection.
• Build and deliver Generative AI and LLM-powered applications using modern AI frameworks.
• Lead experimentation cycles, defining hypotheses, running evaluations, and iterating on models before production deployment.
• Develop robust ML pipelines using AWS services and MLOps tooling.
• Implement experiment tracking, model versioning, monitoring, and reproducibility best practices.
• Build feature engineering pipelines and contribute to feature store development.
• Monitor production models, optimise performance, and drive continuous improvements.
• Apply Responsible AI principles, including explainability, governance, and fairness.
• Mentor junior engineers and contribute to technical leadership across the team.

• Strong commercial experience developing machine learning solutions using Python.
• Experience with frameworks such as:
• PyTorch
• TensorFlow
• Scikit-learn
• XGBoost
• Experience deploying ML solutions on AWS, including services such as SageMaker, Lambda, and S3.
• Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data Version Control (DVC).
• Experience developing LLM or Generative AI applications, including RAG architectures and prompt engineering.
• Knowledge of LLMOps frameworks such as LangChain, LangGraph, or LangSmith.
• Experience taking ML models from experimentation into production environments.
• Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.

Desirable Experience

• AI agents and agentic workflows.
• Vector databases such as Pinecone, Weaviate, or pgvector.
• Feature stores including Feast or AWS Feature Store.
• Docker, Kubernetes, or Amazon ECS.
• Infrastructure as Code using Terraform or CloudFormation.
• Big data processing technologies such as Spark or Dask.
• Experience working within regulated or highly secure environments.

• Work on meaningful AI programmes supporting critical national security missions.
• Exposure to cutting-edge Machine Learning, LLM, and Generative AI technologies.
• Flexible hybrid working with a strong focus on work-life balance.
• Dedicated career development, mentoring, and technical progression.
• Competitive salary, annual bonus, private healthcare, enhanced pension, generous annual leave, and a comprehensive benefits package.