This is an exciting time to join a team to help pioneer both customer's and own an AI adoption journey. Not only will you be directly making a huge impact through the solutions you develop, you’ll be doing it for an organisation who makes a huge impact to the security of the UK.

Core Duties

• Design and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applications

• Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements

• Transition validated experiments into production-ready solutions, working closely with other engineers on 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 production monitoring

• Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails

• Design feature engineering approaches and contribute to feature store development

• Support production models through monitoring, performance analysis, and continuous improvement

• Apply responsible AI practices, including model explainability and fairness assessment

• Present experiment findings and production outcomes to stakeholders, articulating operational and strategic value

• Mentor junior colleagues and share learnings across the team

About You

You will have experience in many of the following:

• Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow

• Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments

• Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation

• Proven track record transitioning models from experimentation to production with appropriate governance and quality controls

• Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control)

• Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures

It Would Be Great If You Also Had Experience In Some Of These, But If Not We’ll Help You With Them

• Experience with advanced LLM techniques: agents, tool use, and agentic workflows

• Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications

• Experience with feature stores (Feast, AWS Feature Store)

• Experience with containerisation (Docker) and orchestration (Kubernetes, ECS)

• Familiarity with Infrastructure as Code (Terraform, CloudFormation)

• Experience with data processing frameworks (Spark, Dask) for large-scale workloads

• Understanding of data governance and compliance frameworks