You will join a cross-functional team dedicated to delivering robust, scalable, and high-quality data and machine learning solutions.

You will work closely with data scientists, data analysts, and engineers to operationalize and scale ML models, ensuring seamless deployment, monitoring, and performance across the organization.

Your expertise will directly support business-critical areas such as supply chain optimization and marketing analytics, by building and maintaining the infrastructure that allows machine learning innovation to thrive.

ML Ops Engineer (with partial Data Engineering background)

Your responsibilities

• Optimize, standardize, and implement data science and machine learning solutions at scale within cloud-based environments (Azure).
• Participate in the end-to-end lifecycle of data science projects by applying DevOps principles, CI/CD pipelines, model and experiment management, and best practices in code quality.
• Collaborate closely with data scientists to productionize already developed models, ensuring reliability, scalability, and performance.
• Work with engineering teams to improve data consumption, model deployment pipelines, and overall ML infrastructure.
• Design and lead activities related to monitoring, troubleshooting, debugging, and incident management for ML pipelines.
• Serve as a trusted advisor and advocate on topics related to ML Ops architecture, infrastructure design, automation, and deployment strategies.

Our requirements

• 2+ years of professional experience in ML Ops or ML Engineering, particularly in productionizing and scaling ML models, with a strong focus on supporting data scientists / researchers (not directly involved in research or model creation).
• Proven experience in Azure ML, Databricks, or similar cloud-based ML environments.
• Solid knowledge of CI/CD, infrastructure-as-code (IaC), containerization (Docker, Kubernetes), and monitoring frameworks.
• Experience with Python, SQL, and relevant data engineering concepts (data pipelines, orchestration, ETL).
• Familiarity with MLflow, DVC, Airflow, or similar tools used in ML lifecycle management.
• Understanding of software engineering best practices – testing, code reviews, version control (Git), and documentation.
• Excellent collaboration and communication skills, with the ability to bridge the gap between data science and engineering disciplines.
• Partial experience in Data Engineering (data ingestion, transformation, and pipeline optimization).
• Exposure to MLOps for deep learning or LLM-based models, even if not directly building them.
• Experience with monitoring and observability tools (e.g., Prometheus, Grafana).