About This Role
A senior engineering role for an ML practitioner who is as strong on operations and deployment as they are on modelling. You will own the production reliability of ML systems, build reusable pipeline frameworks, and work as the technical bridge between data science, DevOps, and IT teams.
What You Will Do
- Design and manage end-to-end ML pipelines using Azure ML, Databricks, and PySpark for large-scale data processing and model training
- Build and maintain automated CI/CD pipelines using GitHub Actions with SonarQube integration for code quality and security
- Develop reusable, modular ML pipeline templates to drive deployment efficiency across the enterprise
- Containerise and deploy ML models using Azure Kubernetes Service (AKS) and Docker, ensuring high availability and auto-scaling
- Design and manage secure APIs for ML model interaction with downstream applications
- Perform model lifecycle management, optimisation, data drift monitoring, and automated data refresh checks
- Implement cost-monitoring strategies for high-compute training and deployment phases
- Maintain detailed technical documentation for workflows, pipeline templates, and optimisation strategies
- Act as the technical liaison between Data Scientists, DevOps, and IT teams across Dev, QA, and Production environments
What You Need to Succeed
- Bachelor's degree in Engineering, Computer Science, or related field
- 5+ years of experience with deep focus on the Azure MLOps toolstack
- Proven track record of deploying and maintaining ML models in high-scale production environments
- Hands-on expertise with Azure Machine Learning and Databricks
- Strong command of Kubernetes (AKS) and API-based deployment platforms
- Solid grasp of DevOps practices and containerisation with Docker
- Experience with code quality automation tools including SonarQube
- Exceptional problem-solving skills with the ability to operate in a fast-paced collaborative environment
What Will Give You an Edge
- Familiarity with broader solution architecture principles
- Azure certifications: AI-900, DP-100, or AZ-305
- Experience with PySpark and large-scale distributed data processing
What Qfyre Offers
- Placement in a high-impact MLOps role within an enterprise AI programme
- Genuine ownership of production ML infrastructure, not a support role
- Market-competitive compensation with flexibility on hybrid working
- Exposure to cutting-edge Azure AI toolstack at scale
Skills and Technologies
Azure MLMLOpsDatabricksAKSCI/CDAI
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