MLOps Engineer

• Benefits package (medical care, sports card, life insurance),
• Long-term cooperation,
• Flexible working hours, Polish time zone.

• 2+ years of professional experience in ML Ops or ML engineering, particularly in productionizing and scaling ML models,
• Broad familiarity with Azure cloud environment and Databricks, including its setup and maintenance as an ML platform,
• Advanced proficiency with Python and SQL in version control systems (git) plus their use in building ML & data pipelines,
• Proven experience with software development best practices including testing, continuous integration, and DevOps tools,
• Good understanding of data science lifecycle and the way data scientists work to deliver value,
• Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.),
• Attention to clarity of code, ease of development, and correctness of implementations
• Business-ready command of English, written and spoken.

Responsibilities

• Optimize, standardize and implement data science and machine learning solutions at scale and in cloud-based environments (Azure),
• Participate in the end-to-end lifecycle of data science projects through the use of DevOps, code, experiment and model management, CI/CD and further industry best practices,
• Write well-designed, testable, efficient code,
• Work closely with engineering to continuously improve the way we consume data and deploy models in production,
• Design and lead on monitoring, troubleshooting, debugging and incident management for our ML pipelines,
• Be a trusted advisor and evangelist to the team and stakeholders on various aspects of ML Ops, from scaling and throughput, to infrastructure and deployment strategies.