Machine Learning Engineer

Machine Learning Engineer / Junior Machine Learning Engineer

Level: Junior ML Engineer (£56,000) | ML Engineer (£65,000)

Location: London

About the Role

Our client is a global defence, security, and intelligence consultancy delivering high-assurance digital, cyber, and AI solutions into some of the UK’s most critical national security missions.

They are growing their National Security AI capability in London and are hiring Machine Learning Engineers at both junior and mid-level. These roles sit within a specialist team delivering applied machine learning and GenAI solutions across defence, space, and wider government environments.

The work spans exploratory research, rapid prototyping, and full production deployment. You will be solving complex, real-world problems using modern ML and LLM techniques, working on sensitive, high-impact systems where quality, governance, and operational readiness genuinely matter.

What You’ll Be Doing

• Designing and developing machine learning models across forecasting, classification, and anomaly detection use cases.
• Building and deploying GenAI and LLM-enabled solutions, including prompt-driven and RAG-based architectures.
• Running structured experimentation cycles, defining hypotheses, designing experiments, and evaluating results within governance constraints.
• Transitioning validated experiments into production-ready systems in collaboration with software and platform engineers.
• Building and maintaining ML pipelines on AWS using modern MLOps and LLMOps tooling.
• Implementing experiment tracking, model versioning, and reproducibility with full auditability.
• Supporting production models through monitoring, evaluation, and continuous improvement.
• Applying responsible AI practices including explainability, validation, and fairness assessment.
• Communicating outcomes and trade-offs clearly to technical and non-technical stakeholders.

Ideal Background

You will have experience in many of the following areas. Expectations scale by level.

• Hands-on experience building ML models in Python using libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Experience deploying ML workloads on AWS, including services such as SageMaker, Lambda, and S3.
• Understanding of experiment design, evaluation techniques, and statistical reasoning.
• Experience taking models from experimentation into production environments.
• Familiarity with MLOps tooling such as MLflow, Weights & Biases, or Data Version Control.
• Experience developing or integrating LLM and GenAI applications, including prompt engineering and retrieval-augmented generation.
• Ability to work across disciplines and communicate technical findings clearly.
• A pragmatic mindset and good judgement about when machine learning is and is not the right solution.

Nice to Have

• Experience with advanced LLM techniques such as agents, tool use, or agentic workflows.
• Experience with vector databases for RAG use cases.
• Familiarity with feature stores, containerisation, or orchestration platforms.
• Exposure to infrastructure-as-code tooling such as Terraform or CloudFormation.
• Experience with large-scale data processing frameworks.
• Background working in regulated or high-assurance environments.

Clearance Reality

Security clearance is required for this role. Candidates who are not currently cleared must be eligible and willing to go through the process.

What You’ll Receive

• Salary up to £65,000 for ML Engineer and up to £56,000 for Junior ML Engineer.
• Hybrid working with flexibility around core hours.
• 25 days annual leave with options to buy, sell, or carry over.
• Competitive pension and flexible benefits package.
• Company bonus scheme.
• Dedicated career management and structured progression pathways.
• Access to internal communities focused on diversity, inclusion, and wellbeing.

Who This Role Suits Best

This role suits engineers who want to work on meaningful, production-grade ML systems rather than proofs of concept. It is a strong fit for candidates who enjoy balancing experimentation with operational discipline and want their work to have tangible national impact.