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

Square One Resources is hiring for a role within the Data Science Hub Europe team, focused on delivering scalable, high-performance data and machine learning solutions supporting supply chain and marketing domains. The project involves building and maintaining a modern ML and analytics platform using Microsoft Azure and Databricks technologies.

• Minimum 2+ years of experience in MLOps, ML Engineering, or similar production-focused ML role
• Strong hands-on experience with Machine Learning in production environments
• Experience with Microsoft Azure cloud platform
• Experience with Databricks (setup, maintenance, and ML workflows)
• Advanced level Python and SQL skills
• 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)

Nice to Have:

• 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

• 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