ML Ops Engineer

Seargin is a dynamic multinational tech company operating in 50 countries. At Seargin, we drive innovation and create projects that shape the future and greatly enhance the quality of life. You will find our solutions in the space industry, supporting scientists in the development of cancer drugs, and implementing innovative technological solutions for industrial clients worldwide. These are just some of the areas in which we operate.

Currently, for the new Seargin project, we are looking for MLOps Engineer.

About the job

• Form: B2B contract
• Location: Remote from Poland

Key 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

Qualifications:

Must have:

• 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

Preferred:

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

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