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.