Senior Machine Learning Engineer (Maternity Leave Cover)
Posted
Summary
A 12-month maternity leave cover role building and productionising machine learning models on local-authority data: designing training pipelines, LLM/RAG and embedding-based models, and containerised services deployed into Xantara's OneView platform. Core stack: Python, ML frameworks, SQL, Docker/FastAPI, with Azure cloud a bonus.
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.
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 multicomponent 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.
We’d love to hear from you if you have:
3–5+ years’ experience in machine learning engineering, taking models from development into production.
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, Fast API)
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
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 at least 1-2 days per week. Some travel is also required for on-site client engagements as needed.
**Please note this is a 12 month Maternity leave cover opportunity**
- Competitive salary reviewed annually
- Work for a passionate, mission-driven company solving society’s big problems
- Work flexible hours around life commitments with a focus on delivering company value rather than hours worked
- Ability to work remotely (excluding face-to-face Team Meetings and client meetings)
- 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