MLOps Engineer (DevOps)

We are SCC – Specialist Computer Centres – Europe’s leading provider of IT services and solutions.

We are looking for an MLOps/ DevOps Engineer to bridge the gap between AI development and

production at SCC. You will work closely with AI Engineers and Data Scientists to take AI systems

through their full lifecycle, from development and testing through to deployment, monitoring, and ongoing reliability.

This is an operational role with deep technical roots. Where the AI Engineer builds the intelligence and the Data Scientist supplies the data, you own the infrastructure, pipelines, and processes that make it all run consistently in production. You will collaborate across data science, engineering, IT operations, and business stakeholders, translating between those worlds and keeping AI systems healthy at every stage. Given that SCC works with foundation models via API rather than training models in-house, this role focuses less on training infrastructure and more on agent deployment, RAG pipeline operations, system observability, and the scalable delivery of AI initiatives across the organisation.

Main Duties of Job:

• Oversee and maintain AI and ML model pipelines from development through to production

• Review and approve changes to AI systems, ensuring quality gates are met before

deployment

• Monitor testing outcomes and validate that AI system behaviour meets defined standards

• Ensure model artefacts, prompt versions, configurations, and dependencies are properly

managed and version controlled.

• Deploy AI systems — including agents, RAG pipelines, and API services — to production

environments using CI/CD practices

• Use containerisation and orchestration tools to manage scalable, reliable AI service delivery

• Manage deployment configuration, environment variables, and secrets across dev, staging,

and production

• Support the integration of AI services with enterprise platforms and downstream applications.

• Monitor live AI systems for error rates, response times, latency, and resource consumption

• Implement automated alerting and anomaly detection for production AI services

• Track and report on AI system health, usage, and performance to stakeholders

• Implement automated model retraining or prompt refinement triggers where applicable

• Maintain data pipelines that feed AI systems, ensuring freshness, quality, and reliability

• Enforce data retention, audit logging, and compliance requirements across all AI components.

Skills, Knowledge & Experience:

Required

• 3–5 years in a DevOps, platform engineering, or MLOps role

• Demonstrated experience deploying and operating AI or data services in a cloud environment

• Strong understanding of CI/CD, containerisation, and infrastructure management

• Comfortable working across both technical and operational domains simultaneously

• An organised, process-driven mindset, you bring structure to complex, multi-team workflows

• Strong collaboration and communication skills; this role succeeds through influence across

teams as much as technical execution

• Familiarity with AI systems sufficient to understand what you are deploying and why it matters.

• Ability to work well individually as well as in a team across multiple locations.

• Good English communication skills, both verbal and written.

Advantageous

• Has knowledge of working collaboration with AI and Data team to develop fully DevOps

solution to huge system.

• Experience with AI governance frameworks (e.g. EU AI Act auditability requirements).

Report Line

• Report to VN/UK Technical lead.