Snr Machine Learning Engineer - Client facing - FTC

Position: Snr Machine Learning Engineer - Client facing - 12 months FTC
Location: Greater London

The Machine Learning Engineer role sits within Client Delivery, embedded in the Data & Analytics Consulting (DACs) team. A technical, client‑facing group of Data Scientists and ML Engineers responsible for building and ope rationalising advanced machine learning and AI.

This is a 12 months contract with potential to become a perm role afterwards.

Machine learning and AI engineering

• Design, train and optimise predictive models (gradient boosted trees, temporal and embedding-based models), with reproducible training, evaluation and monitoring pipelines.
• Product ionise models to run reliably at scale in client environments.
• Build and integrate LLM components (embedding pipelines, RAG, agentic systems) and NLP models for extraction, classification and risk prediction.

Data engineering

• Own versioned, validated data flows for modelling and inference, including schema design and SQL optimisation for large training and scoring workloads.
• Extract data from client source systems and build cohort logic that is accurate and interpretable.

Technical delivery

• Lead the modelling and data engineering components of client projects alongside DACs and Business Consultants, resolving complex modelling and pipeline issues.
• Configure One View components linked to modelling outputs (risk logic, summaries, scoring pathways) and feed engineering improvements back into the platform.

Knowledge sharing and technical leadership

• Act as SME for machine learning and AI within DACs.
• Mentor DACs on Python, modelling practice and debugging.
• Produce documentation, templates and reusable components that raise delivery standards.

• 3–5+ years’ experience in machine learning engineering, taking models from development into production.
• Strong Python skills and experience with modern ML frameworks, including training and evaluating tree-based, temporal, NLP or LLM-based models.
• Experience building reproducible training and evaluation pipelines, and containerising and deploying models (for example, Docker, FastAPI).
• Strong SQL, a solid grounding in relational databases, schemas and data transformations (ideally DBT), and experience preparing data for model training and scoring.
• Hands‑on AI experience: embeddings, vector databases or RAG workflows, and NLP or sequence models applied to real-world datasets.
• Experience delivering technical work to clients or stakeholders, including defining data requirements and troubleshooting in real time.
• Clear communication: you explain technical concepts simply and work closely with data scientists, engineers and consultants.

Bonus points if you have:

• Experience with Azure ML, AKS or similar cloud environments.
• Experience with public sector datasets or analytical workflows.

Location –

This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office 1-2 days per week. Some travel may be required for on‑site client engagements.