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Snr Machine Learning Engineer - Client facing - 12 months FTC

Xantura

Skills

Required

  • tree-based / temporal / NLP / LLM-based models
  • AI
  • Python
  • SQL
  • NLP or
  • machine learning engineering
  • modern ML frameworks
  • containerising and deploying models
  • Docker
  • FastAPI
  • relational databases
  • schemas
  • data transformations
  • DBT
  • embeddings

Preferred

  • machine learning
  • ML
  • Azure
  • AKS
  • cloud environments
  • public sector datasets / analytical workflows
  • LLM
  • RAG
  • data engineering

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.

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.

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.
  • 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.
  • 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.

Preferred

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

Benefits

  • Training and development opportunities
  • 25 days annual leave (plus bank holidays)
  • Flu Vaccinations, Eye Test and contribution towards Glasses for VDU use
  • Mental health and wellbeing support
  • Remote GP access
  • Counselling/therapy
  • Physiotherapy

Generous enhanced parental leave policies

Cycle to work scheme

Medical second opinions