Senior ML Ops Engineer

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.

What are we looking for?

• 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.

• Deploying, scaling, and troubleshooting containerised workload on Kubernetes in production
• Building and maintaining CI/CD pipelines (Azure DevOps or equivalent) for automated testing, building, and deployment of ML services
• Implementing infrastructure-as-code (Terraform, Bicep or Pulumi)
• Implementing monitoring and observability for production systems, including metrics, altering, logging, and dashboarding (e.g. Prometheus, Grafana)
• Pipeline orchestration using Dagster, Airflow, Prefect, or similar

• 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. 
• Experience setting up, and optimising vector databases, e.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).

What can we offer you?

• 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