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About the Role
The Machine Learning Engineer role sits within Client Delivery, embedded in the Data & Analytics Consulting (DACs) team. This is a technical, client-facing group of Data Scientists and ML Engineers responsible for building and operationalising advanced machine learning and AI. This is a 12-month contract with potential to become a permanent role afterwards.
Responsibilities
- Design, train and optimise predictive models (gradient boosted trees, temporal and embedding-based models), with reproducible training, evaluation and monitoring pipelines.
- Productionise 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.
- 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.
- Lead the modelling and data engineering components of client projects alongside DACs and Business Consultants, resolving complex modelling and pipeline issues.
- Configure OneView components linked to modelling outputs (risk logic, summaries, scoring pathways) and feed engineering improvements back into the platform.
- 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.
Requirements
- 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.
- Experience with Azure ML, AKS or similar cloud environments.
- Experience with public sector datasets or analytical workflows.
Skills
- Python
- Machine Learning
- AI
- Gradient Boosted Trees
- Temporal Models
- Embedding Models
- LLM
- NLP
- SQL
- Docker
- FastAPI
- DBT
- Embeddings
- Vector Databases
- RAG
- Azure ML
- AKS
Location
- London
- Borough
Work Type
- Hybrid
- Full Time
- Contract
Experience Level
- 3-5+ years
Salary/Compensations
- Competitive salary reviewed annually
Benefits
- Training and development opportunities
- 25 days annual leave (plus bank holidays)
- Company pension
- Private medical insurance
- Generous enhanced parental leave policies
- Cycle to work scheme
- Flu Vaccinations
- Eye Test and contribution towards Glasses for VDU use
- Employee Assistance Programme
- Mental health and wellbeing support
- Remote GP access
- Counselling/therapy
- Physiotherapy
- Medical second opinions
About the Company
- 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 operationalising advanced machine learning and AI