Intermediate Machine Learning Engineer - BEES Data
External
Abinbev · Campinas, BrazilPrepare for this interview
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About the role
AB InBev is the leading global brewer and one of the world's top 5 consumer product companies. With over 500 beer brands, we're number one or two in many of the world's top beer markets, including North America, Latin America, Europe, Asia, and Africa. About AB InBev Growth Group Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world. In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft. We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.
Responsibilities
- Implement and extend ML platform capabilities (training jobs, inference services, batch and online serving) following team architecture, standards, and best practices.
- Develop and maintain components of the ML model development workflow (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
- Build and operate observability for models in training and production-monitoring, logging, and alerting for performance, quality, and drift-in collaboration with platform and SRE partners.
- Support optimized model deployments (scaling, resource allocation, inference tuning) to meet cost, quality, and SLA targets.
- Troubleshoot pipeline and serving issues, document solutions, and share learnings with the team.
Requirements
- Bachelor's degree in computer science, engineering, mathematics, or another quantitative field.
- Practical experience with ML platform components (e.g., feature pipelines, model registries, training and inference workflows).
- Solid software engineering fundamentals: clean code, testing, CI/CD, and maintainable system design.
- Python , PySpark , and SQL . Exposure to Java is a plus.
- Experience with Kubernetes, Databricks, Terraform, Azure DevOps (Git), Azure Cloud , and ML frameworks/libraries such as Scikit-learn, PyTorch, TensorFlow, ONNX , and serving tools ( BentoML, Kedro, Seldon, KServe, Triton Inference Server , etc.).
- Comfort collaborating across teams, communicating technical tradeoffs clearly, and learning from senior engineers on architecture and platform decisions.
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