Intermediate Machine Learning Engineer

Join us to build the future of B2B commerce!
BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.
Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!

• →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.

• 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.