MLOps

• 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

• Salary up to 130 zł/h netto
• Additional benefits package (medical healthcare, sports membership, etc.)
• Possibility to cooperate with an international company with a stable market position
• Remote position with occasional visits in the office every 1-2 months
• 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 spoke

Nice to have:

• Experience in a release on production ML tools involving a user interface, e.g. Dash, Shiny R, Streamlit
• Experience in Azure cloud resource set up for purposes of data solutions
• Great communication skills to guide audiences of a broad technical knowledge range through complex ML Ops topics
• Practice coaching / mentoring younger team members in your expertise areas