MLOps Engineer

Job summary

The MLOps Engineer will support the Data Science team in deploying, operating, and maintaining machine learning and AI solutions in production. Key responsibilities include implementing production workflows, building CI/CD pipelines, and diagnosing production issues, with a focus on Databricks and technologies like Python, SQL, and Azure DevOps.

Benefits

Qualifications

• Strong Python and SQL skills, with experience using PySpark in distributed data environments
• Experience with Azure Databricks and the Databricks ecosystem
• Experience deploying, operating and monitoring machine learning models in production
• Experience with CI/CD and software engineering practices for data or ML applications
• Skill in setting up unit and integration testing frameworks for AI/Machine learning projects
• Experience with data processing performance optimisation and tuning
• Understanding of governance/guardrails for AI agents

Responsibilities

• Partner with Data Science to take models from experimentation through validation, deployment and ongoing production management
• Implement and maintain production workflows for data ingestion and processing, model execution, retraining, testing and deployment
• Build and maintain CI/CD pipelines and controlled release processes for machine learning and AI workloads
• Build and maintain robust data and feature pipelines required by machine learning and AI solutions
• Diagnose production issues and work with Data Science and Engineers to resolve model, data and platform problems
• Implement access controls, security and governance within Databricks
• Support operationalising of Generative AI solutions, including LLM applications and RAG
• Contribute to reusable templates, tooling and MLOps practices for consistent model deployment
• Establish best practices and configure state of the art tooling for production standards
• Support performance optimisation and efficient use of Databricks and cloud infrastructure

Skills

Certifications

Relocation