As a key member of the Data Science Hub, you will join a specialized team responsible for delivering scalable, high-performance data and machine learning solutions across the organization. The project focuses on building and maintaining a modern ML & analytics platform supporting critical business functions in supply chain and marketing domains.

You will collaborate closely with data scientists, data engineers, and analysts to design, deploy, and operate machine learning systems in production, leveraging cloud-native technologies within the Microsoft Azure ecosystem and Databricks platform.

The role is centered on enabling end-to-end ML lifecycle management, ensuring reliability, scalability, and operational excellence of ML pipelines.

MLOps Engineer (Azure / Databricks / Python ML Platform Engineer)

Your responsibilities

• Design, build, and optimize scalable machine learning solutions in cloud-based environments (Azure)
• Support end-to-end ML lifecycle: development, deployment, monitoring, and maintenance of ML models
• Implement DevOps practices for ML workflows including CI/CD, version control, testing, and automation
• Develop and maintain efficient, testable, and production-grade Python code for ML pipelines
• Collaborate with data engineering teams to improve data ingestion, transformation, and model deployment processes
• Design and implement monitoring, alerting, troubleshooting, and incident management for ML pipelines
• Act as an ML Ops subject matter expert, advising stakeholders on scalability, infrastructure, and deployment strategies
• Ensure best practices in ML system design, reliability, and performance optimization

Our requirements

• Minimum 2+ years of experience in MLOps, ML Engineering, or similar production-focused ML role
• Strong hands-on experience with:
• Machine Learning (ML) in production environments
• Microsoft Azure cloud platform
• Databricks (setup, maintenance, and ML workflows)
• Python (advanced level) and SQL
• Experience working with Git and CI/CD pipelines in production environments
• Solid understanding of ML lifecycle and collaboration with data science teams
• Experience with DevOps practices, testing frameworks, and software engineering standards
• Familiarity with Agile methodologies (Scrum / Kanban)
• Strong focus on code quality, scalability, and maintainability
• Business-level English (written and spoken)
• Experience with ML applications involving UI components (e.g., Streamlit, Dash, Shiny)
• Hands-on experience with Azure infrastructure setup for data/ML platforms
• Experience with Snowpark and productionizing ML/AI solutions in Snowflake ecosystem
• Strong communication skills for explaining complex ML Ops topics to mixed technical audiences
• Experience mentoring junior engineers or contributing to team capability development