Machine Learning Engineer

Machine Learning Engineer

Location: London (Hybrid, approx. 3 days per week onsite)

Salary: Up to £65,000 (depending on experience)

NOTE: Candidates must be eligible for UK Developed Vetting (DV)

This is an opportunity to work at the forefront of applied AI within highly secure and mission-critical environments. You’ll be contributing to the development of advanced machine learning and generative AI solutions that deliver real-world impact across complex, high-stakes domains.

You will be embedded within a multidisciplinary engineering team, working across the end-to-end delivery of machine learning systems. This includes building models, running experiments, and deploying scalable solutions using modern cloud and MLOps tooling.

Key Responsibilities Include:

• Designing and developing machine learning models (e.g. classification, forecasting, anomaly detection)
• Supporting experimentation cycles: hypothesis definition, testing, and evaluation
• Contributing to the deployment of models into production environments
• Building and maintaining ML pipelines using AWS services
• Assisting in the development of GenAI/LLM-based applications (e.g. prompt engineering, RAG pipelines)
• Implementing best practices in experiment tracking, model versioning, and reproducibility
• Monitoring model performance and supporting continuous improvement
• Collaborating with engineers, data scientists, and stakeholders across the delivery lifecycle

Required Skills & Experience

• Minimum 1+ year of commercial experience in machine learning or data science
• Strong Python skills with experience in ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost
• Exposure to AWS services (e.g. S3, Lambda, SageMaker)
• Understanding of core ML concepts: model evaluation, validation, and experimentation
• Familiarity with MLOps or experiment tracking tools (e.g. MLflow, Weights & Biases)
• Awareness of LLMs or generative AI concepts
• Strong problem-solving ability and willingness to learn in a fast-paced environment
• Ability to communicate technical concepts clearly

Desirable Experience

• Experience with LLM frameworks (e.g. LangChain)
• Knowledge of vector databases or RAG architectures
• Exposure to Docker or containerised environments
• Familiarity with data processing tools (e.g. Spark, Dask)
• Understanding of CI/CD or infrastructure as code

If you are an ambitious Machine Learning Engineer looking to build real-world AI systems in a high-impact environment, apply today.