Senior MLOps Engineer

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. 

Key Responsibilities & Skills: 

• 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. 

 Essential Technical Skills: 

• Programming: Python, SQL, PySpark, Bash 

• ML Frameworks: TensorFlow, PyTorch, Scikit-learn 

• MLOps & Model Management: MLflow, Model versioning, CI/CD