Senior Machine Learning Engineer (Maternity Leave Cover)
Machine Learning EngineerData
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Quick Summary
Overview
The Machine Learning Engineer role sits within Client Delivery, embedded in the Data & Analytics Consulting (DACs) team – a technical,
Technical Tools
Machine Learning EngineerData
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
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.
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.
Location & Eligibility
Where is the job
London, United Kingdom
Hybrid — some on-site time required
Who can apply
GB
Listing Details
- First seen
- September 25, 2026
- Last seen
- October 3, 2026
Posting Health
- Days active
- 7
- Repost count
- 1
- Trust Level
- 28%
- Scored at
- October 3, 2026
Signal breakdown
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