Machine Learning Ops (MLOps) Engineer

We are looking for an experienced MLOps Engineer with 3-12 years of experience in deploying, scaling, and maintaining machine learning models in production. You will work closely with Data Scientists, ML Engineers, and DevOps teams to build robust ML pipelines, automate model operations, and ensure reliable ML lifecycle management. This is a fully remote role within the UK., * Design, build, and manage end-to-end MLOps pipelines for model training, deployment, monitoring, and versioning.

• Automate ML workflows using tools such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML.
• Deploy and maintain models on cloud platforms (AWS, Azure, GCP) using containerized environments.
• Collaborate with Data Scientists to operationalize ML models, improve reproducibility, and optimize model performance.
• Implement CI/CD for ML (CI/CD/CT) using GitHub Actions, GitLab CI, Jenkins, or similar.
• Manage model registries, feature stores, and metadata tracking.
• Monitor production models for performance drift, latency, and reliability.
• Develop scalable infrastructure using Docker, Kubernetes, Terraform, Helm.
• Ensure governance, security, and compliance across ML pipelines and data flow.
• Troubleshoot issues in production ML systems and support continuous improvement.
• Participate in Agile/Scrum ceremonies and contribute to architectural discussions.

Do you have experience in Terraform?, * 3-12 years experience in MLOps, ML Engineering, or DevOps/Data Engineering with ML focus.

• Strong programming skills in Python (required).
• Hands-on experience with ML orchestration tools such as:
• MLflow
• Kubeflow
• Airflow / Prefect
• SageMaker / Azure ML / Vertex AI
• Strong knowledge of CI/CD pipelines, DevOps tooling, and automation.
• Experience working with Docker & Kubernetes in production.
• Proficiency with at least one cloud platform (AWS preferred, or Azure/GCP).
• Understanding of ML lifecycle, model training, inference, and monitoring.
• Experience with infrastructure-as-code (Terraform / CloudFormation / ARM).
• Strong understanding of data pipelines and ETL concepts.
• Familiarity with model monitoring tools (Prometheus, EvidentlyAI, Grafana).

Job Types: Full-time, Permanent

Pay: £55,000.00-£120,000.00 per year

Benefits:

• Work from home