Machine Learning Engineer (CS)

The Machine Learning Engineer role sits within Customer Success in the Technical Operations team – a group of data and ML engineers responsible for optimising advanced machine learning components across Xantura’s projects.

Department: Customer Success

Location: London (Hybrid – office in Borough, 1‑2 days per week, occasional client travel)

Overview: As an ML Engineer, your core work is monitoring, training, evaluating, and productionising 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, optimisation, debugging, and schema‑aligned data preparation.

Key Responsibilities:

• Design, train, and optimise predictive models using advanced architectures such as gradient‑boosted trees, temporal, and embedding‑based models.
• Build robust training, evaluation, and monitoring pipelines to ensure model quality, reproducibility and auditability.
• Implement feature engineering, hyperparameter tuning, debugging, and performance optimisation.
• Productionise models so they run reliably and efficiently at scale in client environments.
• 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.
• Lead modelling and data‑engineering components of client projects alongside Data Engineers.
• Build and validate cohort logic to ensure accuracy, interpretability and alignment with client needs.
• Troubleshoot and resolve complex modelling and pipeline issues in BAU.
• Optimise and integrate LLM‑based components including embedding pipelines, RAG workflows and text‑analysis models.
• Develop and deploy agentic and multi‑component AI systems using modern ML frameworks.
• Engineer high‑performance NLP and sequence models for information extraction, classification and risk prediction.
• Configure advanced OneView components linked to modelling outputs such as risk logic, summaries and scoring pathways.
• Act as an SME for machine learning, AI, and model engineering within the team.
• Mentor Data Engineers on Python, modelling best practice, data engineering fundamentals and debugging approaches.
• Produce documentation, templates, and reusable components to raise engineering standards across delivery.

Qualifications:

• 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 (Docker, FastAPI).
• Strong SQL and experience with relational databases.
• Understanding of schemas, data transformations and (ideally) dbt.
• Experience preparing data for model training and scoring.
• Experience with embeddings, vector databases or RAG‑style workflows.
• Experience applying NLP or sequence models to real‑world datasets.
• Comfortable defining data requirements, discussing modelling decisions, and troubleshooting issues in real time.
• Able to explain technical concepts simply and work closely with data, engineers and customer success.

Bonus Points:

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

Benefits:

• Competitive salary reviewed annually.
• Work for a passionate, mission‑driven company solving society’s big problems.
• Flexible hours around life commitments with a focus on delivering value.
• 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.