Requirements

Operating system

Windows

Our requirements

• Min 3 years of professional experience in MLOps / ML Engineering, with knowledge of best practices in software development, including CI/CD/CT.
• Proficiency and experience in the Azure cloud environment and Databricks, including configuration and maintenance as an ML platform.
• Advanced programming skills, especially in languages such as Python and SQL. Experience with R is a plus.
• Solid understanding of the ML model lifecycle (from training to deployment, monitoring, and decommissioning).
• Hands-on experience in building models and implementing the machine learning lifecycle at scale in complex environments. Practical experience in designing and working with a Feature Store. Experience with testing in the ML process.
• Proficiency in containerization and orchestration technologies, including Docker and Kubernetes.
• Practical knowledge of MLFlow, REST APIs, and Spark.
• Familiarity with tools and processes related to version control, CI/CD, and automation.
• Experience with Terraform is a plus.
• Experience in AI projects is a plus.
• Strong analytical and communication skills, and the ability to work in interdisciplinary teams.

Your responsibilities

• Contribute to designing and delivering secure, efficient, and scalable platform for machine learning.
• Develop, implement, and maintain efficient and scalable CI/CD processes.
• Build system to support real-time model monitoring, anomaly detection, and version management.
• Collaborate with Data Science teams to implement MLOps practices on the cloud platform, including pipeline automation, versioning, CI/CD, and test automation.
• Optimize infrastructure for cost efficiency and support Data Science teams in optimizing solution costs.
• Provide support in troubleshooting and incident management for ML pipelines.
• Work with enterprise architects and owners of other systems to integrate ML solutions hosted on the Analytics Platform with business systems, including BPMN.
• Develop standards and best practices for using and integrating the Analytics Platform.
• Ensure compliance with security and data governance policies.