The Role
Build, optimise, and productionise machine learning and AI models for client environments. Develop reproducible training, evaluation, monitoring, and data pipelines; integrate LLM, NLP, embedding, RAG, and agentic systems; optimise SQL and data flows; and lead technical delivery for client projects. Configure platform modelling outputs, troubleshoot complex issues, mentor colleagues, and create reusable technical standards. The role is hybrid in London, with occasional client travel and potential conversion from a 12-month contract to permanent employment.
Summary Generated by Built In
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
This is a 12 months contract with potential to become a perm role afterwards.
This is a 12 months contract with potential to become a perm role afterwards.
Key Responsibilities
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.
- 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.
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 OneView 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.
What are we looking for?
- 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.
What can we offer you?
- Competitive salary reviewed annually
- 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
At Xantura, we’re on a mission to reduce societal inequality by helping local authorities use data more effectively. Our AI-driven platform empowers frontline workers with the insights they need to prevent complex issues like homelessness or children being taken into care — before they happen. We make this possible by connecting siloed datasets, applying advanced machine learning to enrich the data, and using predictive analytics to identify those most at risk. Our platform then distills this into clear, actionable insights that help frontline staff intervene early and make a real difference. It’s an exciting time to join Xantura. We’re scaling quickly, bringing on new clients, strengthening our platform, and expanding into new areas. While we’re a technology company at heart, our true focus is on improving lives — and we’re looking for people who share that vision.
Skills Required
- 3-5+ years of experience in machine learning engineering, taking models from development into production
- Strong Python skills
- Experience with modern machine learning frameworks and tree-based, temporal, NLP, or LLM-based models
- Experience building reproducible training and evaluation pipelines
- Experience containerising and deploying models, such as with Docker or FastAPI
- Strong SQL and knowledge of relational databases, schemas, and data transformations
- Experience preparing data for model training and scoring
- Hands-on experience with embeddings, vector databases, or RAG workflows
- Experience applying NLP or sequence models to real-world datasets
- Experience delivering technical work to clients or stakeholders
- Clear communication and ability to explain technical concepts simply
- Experience with dbt
- Experience with Azure ML, AKS, or similar cloud environments
- Experience with public sector datasets or analytical workflows
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The Company
What We Do
Xantura is a technology company that provides data sharing and advanced analytics to the public sector, using AI and machine learning to help local authorities improve outcomes for vulnerable people and reduce societal inequality.
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