Snr Machine Learning Engineer - Client facing - 12 months FTC

Xantura; London Borough of Southwark; Hybrid; GBP 85,000 - 115,000; Full time

Benefits offered by this job

Competitive salary

Training and development opportunities

25 days annual leave

Company pension

Private medical insurance

Parental leave policies

Cycle to work scheme

Eye test & glasses support

Employee Assistance Programme

Job summary

Xantura Limited is seeking a Senior Machine Learning Engineer to join the Client Delivery team in London on a 12-month FTC. The role sits in a client-facing DACs group focused on building and operationalising advanced ML and AI solutions for diverse clients.

You will design, train and productionise models, integrate LLM components and supervise data engineering for robust deployments, with potential to transition to a permanent role.

Qualifications

• 3–5+ years’ experience taking models into production.
• Strong Python and ML framework experience.
• Experience building reproducible training pipelines and deploying models.
• Solid SQL and data transformations experience.

Responsibilities

• Design, train and optimise predictive models for clients.
• Productionise models to run at scale in client environments.
• Build and integrate LLM components and NLP models.
• Lead modelling and data engineering within client projects.

Skills

Python

ML frameworks

Containerization

SQL

NLP/LLM

Client engagement

Communication

Tools

Docker

FastAPI

DBT

Azure ML

Job description

Snr Machine Learning Engineer - Client facing - 12 months FTC

Department: Project Delivery

Employment Type: Permanent - Full Time

Location: London

Description

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.

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