Machine Learning Engineer

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 components across Xantura’s projects.

As an ML Engineer here, your core work is designing, training, evaluating, and productionising machine learning models on complex, multi-source datasets from local authorities. You will engineer high-performance training pipelines, build embedding-based and sequence models, implement LLM and RAG workflows, and develop containerised model services that integrate directly into the OneView platform. This includes hands-on work with model architectures, feature engineering, model optimisation, performance debugging, schema-aligned data preparation, and ML-driven interfaces.

This is a role for engineers who want to build real models, ship real systems, and solve real operational ML problems – not just prototypes. You will work directly with production data, client technical teams, and our internal engineering ecosystem to deliver AI components that are robust, scalable, and deployed into live environments.

Key Responsibilities

Machine learning engineering

• Design, train and optimise predictive models using advanced architectures such as gradient-boosted trees, temporal models and embedding-based models.
• Build robust training, evaluation and monitoring pipelines to ensure model quality, reproducibility and auditability.
• Implement feature engineering, hyperparameter tuning, model debugging and performance optimisation.
• Productionise models so they run reliably and efficiently at scale in client environments.

Data engineering

• Own schema-aware data flows for modelling and cohorts; validate, transform and version datasets used in training and inference.
• Manage and evolve database schemas; optimise SQL, indexing and partitioning for large training and scoring workloads.

Technical delivery

• Lead the modelling and data-engineering components of client projects alongside DACs and Business Consultants.
• Acquire and extract data from client source systems.
• Build and validate cohort logic to ensure accuracy, interpretability and alignment with client needs.
• Troubleshoot and resolve complex modelling and pipeline issues throughout delivery.

AI engineering

• Build and integrate LLM-based components including embedding pipelines, RAG workflows and text-analysis models.
• Develop and deploy agentic and multi-component AI systems using modern ML frameworks.
• Engineer high-performance NLP and sequence models for information extraction, classification and risk prediction.

Engineering-level platform configuration

• Configure advanced OneView components linked to modelling outputs such as risk logic, summaries and scoring pathways.
• Contribute modelling innovations, performance insights and engineering improvements back into the platform.

Knowledge sharing and technical leadership

• Act as an SME for machine learning, AI and model engineering within DACs.
• Mentor DACs on Python, modelling best practice, data engineering fundamentals and debugging approaches.
• Produce documentation, templates and reusable components to raise engineering standards across delivery.

What we’re looking for

We’d love to hear from you if you have:

• 3–5+ years’ experience in machine learning engineering, taking models from development into production.
• Strong Python engineering skills and experience with modern ML frameworks.
• Practical experience training and evaluating models (tree-based, temporal, embedding/NLP or LLM-based).
• Ability to build reproducible training and evaluation pipelines.
• Experience containerising and deploying models (e.g., Docker, FastAPI).
• Solid data and database engineering.
• Strong SQL and experience working with relational databases.
• Understanding of schemas, data transformations and (ideally) dbt.
• Experience preparing data for model training and scoring.
• Hands-on AI/LLM experience.
• Working with embeddings, vector databases or RAG-style workflows.
• Experience applying NLP or sequence models to real-world datasets.
• Experience delivering technical work to clients or stakeholders.
• Comfortable defining data requirements, discussing modelling decisions and troubleshooting issues in real time.
• Clear communication and collaborative mindset.
• Able to 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.

About Xantura

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 distils this into clear, actionable insights that help frontline staff intervene early and make a real difference.

The past year has been a period of rapid growth. Our team has doubled, our client base has more than doubled, and our revenue has increased four-fold. We now work with over 20 local councils, supporting them across financial inclusion, homelessness, adult social care, children’s services, and criminal justice.

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.

Location

Our model is hybrid, you can work remotely or from the office. For this role, you would be expected to be able to work from the London office 1–2 days per week as required. Some travel is required for on-site client engagements as needed.