MLOps Engineer

Inside IR35 contract | 3-6 month

1-2 days onsite requirement London or Birmingham

We are seeking an experienced MLOps Engineer with a strong background in DevOps, Data Science, or Machine Learning Engineering, who has hands-on experience productionising ML models.

The focus of the role is building and enabling production-grade ML environments rather than model development itself. Candidates must have deep MLflow experience and proven delivery in real-world client settings.

Key Responsibilities

• Design, build, and maintain end-to-end MLOps environments to support model training, tracking, deployment, and monitoring
• Implement MLflow for: Experiment tracking; Model registry; Model versioning and lifecycle management
• Enable model deployment into production (batch and/or real-time) with robust CI/CD
• Work closely with Data Scientists to transition models from experimentation to production
• Build scalable, secure, and reproducible ML platforms
• Establish best practices around: Model governance; Monitoring and retraining; Environment management
• Integrate with cloud and data platforms such as Databricks, and potentially AWS SageMaker

Essential Experience

• Strong MLOps background, not just theoretical knowledge
• Extensive hands-on MLflow experience (non-negotiable)
• Demonstrable experience productionising ML models for at least 2–3 client engagements
• Background in one or more of: DevOps; Data Science / Machine Learning Engineering; Data Engineering (not required, but acceptable if MLOps-led)
• Experience designing and supporting ML platforms in production environments

Technical Skills (Required / Highly Desirable)

• MLflow
• Databricks
• Cloud platforms (AWS preferred; SageMaker experience a plus)
• CI/CD for ML (e.g. GitHub Actions, GitLab CI, Azure DevOps, etc.)
• Containerisation and orchestration (Docker, Kubernetes)
• Infrastructure as Code (Terraform or similar)
• Python-centric ML workflows

Sponsorship not available for this role.