ML Ops Engineer

Job Description: We are looking for an experienced ML Ops Engineer to join our Data Science Hub Europe team. The successful candidate will play a crucial role in optimizing, standardizing, and implementing machine learning solutions at scale within a cloud-based environment (Azure). The role requires close collaboration with data scientists, analysts, and engineers to build, scale, and operate our ML and analytics platform, ensuring efficiency and high-quality data solutions.

Responsibilities:

• Optimize, standardize, and implement machine learning solutions at scale within cloud-based environments (Azure).
• Participate in the end-to-end lifecycle of data science projects using DevOps best practices, including code, experiment and model management, and CI/CD.
• Develop well-designed, testable, and efficient code to support ML and data pipelines.
• Work closely with the engineering team to improve data consumption and model deployment in production.
• Design and implement monitoring, troubleshooting, debugging, and incident management for ML pipelines.
• Act as a trusted advisor and evangelist on ML Ops best practices, including scaling, infrastructure, and deployment strategies.

Requirements:

• 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 setup and maintenance as an ML platform.
• Advanced proficiency in Python and SQL with version control systems (Git), as well as their application in building ML and data pipelines.
• Strong experience with software development best practices, including testing, continuous integration, and DevOps tools.
• Good understanding of the data science lifecycle and the workflow of data scientists to deliver value.
• Familiarity with agile software development methodologies (SCRUM, Kanban, etc.).
• Strong attention to code clarity, ease of development, and implementation correctness.
• Business-level proficiency in English, both written and spoken.

Nice-to-Have Qualifications:

• Experience in productionizing ML tools involving a user interface, such as Dash, Shiny R, or Streamlit.
• Experience in setting up Azure cloud resources for data solutions.
• Excellent communication skills to guide audiences with diverse technical backgrounds through complex ML Ops topics.
• Experience in coaching and mentoring junior team members in ML Ops and related areas.

Additional Information: As a key member of the Data Science Hub Europe, the consultant will collaborate closely with data scientists, analysts, and engineers to develop, scale, and manage machine learning and analytics platforms. The skills, expertise, and creativity brought to the role will directly influence and support critical business decisions, particularly in areas such as supply chain and marketing.