Department: Project DeliveryLocation: LondonDescriptionThe 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 ResponsibilitiesMachine learning engineeringDesign, 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 deliveryLead the modelling and data engineering components of client projects alongside DACs and Business Consultants.Acquire and extract data from client source systemsBuild and validate cohort logic to ensure accuracy, interpretability and alignment with client needs.Troubleshoot and resolve complex modelling and pipeline issues throughout delivery.AI engineeringBuild and integrate LLM based components including embedding pipelines, RAG workflows and text analysis models.Develop and deploy agentic and multicomponent AI systems using modern ML frameworks.Engineer highperformance NLP and sequence models for information extraction, classification and risk prediction.Engineeringlevel platform configurationConfigure 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 leadershipAct 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 are we 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 frameworksPractical experience training and evaluating models (treebased, temporal, embedding/NLP or LLMbased)Ability to build reproducible training and evaluation pipelinesExperience containerising and deploying models (e.g., Docker, FastAPI)Solid data and database engineeringStrong SQL and experience working with relational databasesUnderstanding of schemas, data transformations and (ideally) dbtExperience preparing data for model training and scoringHandson AI/LLM experienceWorking with embeddings, vector databases or RAGstyle workflowsExperience applying NLP or sequence models to realworld datasetsExperience delivering technical work to clients or stakeholdersComfortable defining data requirements, discussing modelling decisions and troubleshooting issues in real timeClear communication and collaborative mindsetAble to explain technical concepts simply and work closely with data scientists, engineers and consultantsBonus points if you have:Experience with Azure ML, AKS or similar cloud environmentsExperience with public sector datasets or analytical workflowsLocation – This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office at least 1-2 days per week. Some travel is also required for on-site client engagements as needed.What can we offer you?Competitive salary reviewed annuallyWork for a passionate, mission-driven company solving society’s big problemsWork flexible hours around life commitments with a focus on delivering company value rather than hours workedAbility to work remotely (excluding face-to-face Team Meetings and client meetings)Training and development opportunities25 days annual leave (plus bank holidays)Company pensionPrivate medical insuranceGenerous enhanced parental leave policiesCycle to work schemeFlu Vaccinations,Eye Test and contribution towards Glasses for VDU useEmployee Assistance ProgrammeMental health and wellbeing supportRemote GP accessCounselling/therapyPhysiotherapyMedical second opinions