Senior MLOps Engineer

As an MLOps Engineer, you will play a pivotal role in designing, building, and operating the platforms, pipelines, and infrastructure that enable machine learning solutions to move reliably from experimentation to production.

This is a blended role combining MLOps, Data Engineering, and DevOps, well-suited to an engineer who enjoys working across the full ML and data lifecycle.

You will work within dynamic, multi-disciplinary project teams, collaborating closely with data scientists, software engineers, and client stakeholders to deliver scalable, production-ready ML and data solutions across a diverse range of commercial sectors.

This role offers strong opportunities for career development, exposure to real-world ML systems, and continuous learning within a fast-growing engineering team.

• Own and manage the end-to-end lifecycle of machine learning models, including development, training, deployment, monitoring, and continuous improvement.
• Productionise ML models built with PyTorch, TensorFlow, or Scikit-learn , ensuring reliability, scalability, and security.
• Implement model versioning and experiment tracking using MLflow, along with controlled releases, rollback strategies, and continuous improvement workflows.
• Design, build, and optimize robust data pipelines in Databricks, feeding ML models and analytics workloads.
• Develop and maintain scalable data architectures including Azure Data Lake, Azure SQL Data Warehouse, and Synapse Analytics.
• Build and maintain reliable ETL/ELT workflows, ensuring high data quality, accessibility, and performance.
• Collaborate with data scientists and analysts to translate business and data requirements into production-grade ML pipelines.
• Develop and maintain CI/CD pipelines for software, data, and ML workflows using tools such as Azure DevOps, Git, and Jenkins.
• Automate model training, testing, validation, and deployment using MLflow and Databricks Jobs.
• Automate infrastructure provisioning, environment management, and deployment using Terraform, ARM templates, or Ansible.