Job Description

We are seeking an experienced MLOps Engineer (Contract) with a strong background in Machine Learning Engineering, who has hands-on experience productionising ML models for multiple clients.

The focus of the role is building and enabling production-grade ML environments rather than model development itself.

Key Responsibilities

• Design, build, and maintain end-to-end MLOps environments to support model training, tracking, deployment, and monitoring
• 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

Essential Experience

• Strong MLOps background, not just theoretical knowledge
• 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)

• Python-centric ML workflows
• MLflow
• Databricks (strong preference)
• MLOps platform experience (e.g. VertexAI, Sagemaker, etc)
• CI/CD for ML (e.g. GitHub Actions, GitLab CI, Azure DevOps, etc.)

Nice to Have

• Experience working in consulting or client-facing delivery roles
• Exposure to multiple industries or ML use cases
• Model monitoring and drift detection experience