Senior ML Ops Engineer

In this role you will work in the Platform team – a function for the deployment and evolution of the backend platform that underpins the core of the Xantura business. As an MLOps Engineer, you'll own the infrastructure layer that all of Xantura's AI services depend on to operate and scale. You'll be responsible for how ML models and NLP systems are deployed, monitored, and maintained across a growing base of local authority clients, ensuring that what works for one client works reliably for a hundred.

You'll work across every pillar of the AI function - predictive modelling, text analytics, knowledge representation, and agentic AI - building the deployment pipelines, orchestration, and observability that enable the rest of the engineering team to ship with confidence.

Key Responsibilities

Continuously evolve the platform infrastructure powering all AI services (predictive modelling, NLP, knowledge representation, and agentic AI), ensuring reliable, scalable operation across a growing base of local authority clients. Deploy and manage ML models via Azure ML endpoints, batch endpoints , and AKS, enabling resilient, secure model hosting that accelerates client onboarding and ensures models remain performant and monitorable throughout their lifecycle. Ensure all ML systems are transparent, explainable, and auditable, aligned with Responsible AI principles and UK GDPR; essential where AI outputs inform decisions about vulnerable people in health and social care.

Design, build, and maintain production-grade orchestration pipelines (Dagster) supporting model training, inference, and retraining, ensuring data from local authority systems is timely, accurate, and fit for purpose before it reaches ML services. Contribute to organisation-wide AI capability building, sharing best practice with delivery and consulting teams, advising on technical feasibility, and shaping governance standards as the AI function scales. We’d love to hear from you if you have: Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.

4+ years of professional experience in an MLOps, Platform Engineering, or Infrastructure Engineering role supporting ML or data-intensive systems. Strong programming skills and production experience in Python. Expertise in Azure-native MLOps, including model endpoints, pipelines, registries, environments, and compute management.

g. Prometheus, Grafana) Pipeline orchestration using Dagster, Airflow, Prefect, or similar Bonus points if you have: Practical experience with model serving infrastructure – batch and/or real-time inference at scale. Experience operating multi-tenant systems, particularly scaling infrastructure across multiple clients or business units.

Practical experience building and serving production-ready, asynchronous APIs for embedding and/or other compute-intensive services. g. Qdrant, and integrating with other services Proficiency in Python for building high-performance data and model pipelines, with strong software engineering discipline (testing, versioning, CI/CD).

Deep familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Container Registry, Azure DevOps, Azure Blob Storage, Azure Monitor, Azure Key Vault). 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.