Machine Learning Engineer

• PyTorch, TensorFlow or scikit-learn
• LLMs, RAG, agents and evaluation
• ML research and rapid prototyping
• MLOps and production ML pipelines
• Model deployment, monitoring and lifecycle tooling
• AWS, Docker and Linux
• CI/CD and software engineering best practice

The strongest candidates tend to sit somewhere between research and engineering.

You might be taking ideas from papers and applying them to real customer problems, building production ML systems, or working with Platform and Data Engineers to make sure models actually perform reliably outside a notebook.

Why it’s worth a look

The teams I’m supporting offer:

• Genuine ML/AI specialism rather than generic consultancy work
• Exposure across research through to production
• Strong technical peers
• Time and encouragement to keep learning
• Real-world impact rather than endless PoCs
• The chance to work across some of the most interesting secure AI problems in the UK

You don’t need every framework listed above.

Curiosity, strong Python fundamentals and a genuine interest in how ML systems work in the real world matter more than ticking every box.

You’ll need active SC as a minimum, with willingness/eligibility to go through higher levels of clearance long-term.