ML Ops with Azure/Databricks

4 miesięcy temu

Warszawa, Polska Square One Resources Sp. z o.o. Pełny etat

Join our dynamic team at the Data Science where you will contribute to delivering high-quality data solutions that drive critical business decisions in supply chain and marketing. Collaborate with a diverse group of data scientists, analysts, and engineers to build, scale, and operate our cutting-edge machine learning and analytics platform in a cloud-based environment.

ML Ops with Azure/Databricks


Your responsibilities

  • Optimize, standardize, and implement scalable data science and machine learning solutions in Azure cloud environments.
  • Participate in the complete lifecycle of data science projects using DevOps methodologies, including code, experiment, and model management, as well as CI/CD practices.
  • Write well-designed, testable, and efficient code.
  • Collaborate with engineering teams to enhance data consumption and model deployment processes.
  • Design and oversee monitoring, troubleshooting, debugging, and incident management for our ML pipelines.
  • Act as a trusted advisor and advocate for the team and stakeholders on ML Ops aspects, including scaling, throughput, infrastructure, and deployment strategies

Our requirements

  • Minimum of 2 years of professional experience in ML Ops or ML engineering, with a focus on productionizing and scaling ML models.
  • Strong familiarity with Azure cloud environment and Databricks, including setup and maintenance for ML applications.
  • Advanced proficiency in Python and SQL, with experience in version control systems (Git) for building ML and data pipelines.
  • Proven experience with software development best practices, including testing, continuous integration, and DevOps tools.
  • Solid understanding of the data science lifecycle and the operational methodologies of data scientists.
  • Familiarity with agile software development methodologies (SCRUM, Kanban).
  • Attention to code clarity, ease of development, and correctness of implementations.
  • Business-ready proficiency in English, both written and spoken.
  • Experience in productionizing ML tools with user interfaces (e.g., Dash, Shiny R, Streamlit).
  • Familiarity with Azure cloud resource setup for data solution purposes.
  • Strong communication skills to explain complex ML Ops topics to audiences with varying technical backgrounds.
  • Experience in coaching or mentoring junior team members in your areas of expertise.

Aplikuj

 

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