Machine Learning - Platform Engineer
HEINEKEN Global Shared ServicesPoland4 days ago
Full-timeEngineering, Information Technology
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Digital & Technology Team (D&T) is an integral division of HEINEKEN Global Shared Services Center. We are committed to making Heineken the most connected brewery. That includes digitalizing and integrating our processes, ensuring best-in-class technology, and embedding a data-driven culture. By joining us you will work in one of the most dynamic and innovative teams and have a direct impact on building the future of Heineken!
Would you like to meet the Team, see our office and much more? Visit our website:
Your Responsibilities Would Include
Would you like to meet the Team, see our office and much more? Visit our website:
Your Responsibilities Would Include
- building and evolving the MLOps framework for running, monitoring, and deploying ML pipelines
- designing and maintaining platform components to automate and simplify ML workflows
- developing production-grade ML libraries, algorithms, and CLI tooling
- building and maintaining a FastAPI backend to expose results and trigger simulations/optimizations
- automating workflows using Databricks Workflows and integrating with the Azure stack (Azure ML, ADF, Azure Functions, ADLS, Web Apps, Redis, etc.)
- developing and enhancing end-to-end machine learning pipelines
- optimizing data ingestion and feature engineering processes for large-scale applications
- contributing to CI/CD processes with Azure DevOps
- collaborating closely with Data Scientists and Engineers to improve the developer experience
- conducting code reviews and ensuring best engineering practices (testing, standards, modularity).
- at least 3 years of experience as an MLOps or ML Engineer in production environments, combined with software engineering experience
- strong Python programming skills
- experience related to using ML infrastructure at scale
- experience in writing production code for machine learning models
- solid coding skills and software development experience
- experience with Azure DevOps and CI/CD pipelines
- working knowledge of Databricks and PySpark
- ability to design clean, modular APIs and internal tools
- fluency in extracting information from databases and good SQL skills
- understanding of fundamental data science concepts and experience with common tooling and packages used for machine learning.
- experience with FastAPI or similar frameworks
- familiarity with MLflow, or Azure ML pipelines
- experience building internal platforms or tooling for ML/DS teams
- understanding of orchestration patterns and scalable ML infrastructure.
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