Senior Machine Learning Engineer
Job Description
<p> </p> <p><strong><span data-contrast="auto">About BEES</span></strong></p> <p><span data-contrast="auto">Join us to build the future of B2B commerce!</span> <span data-ccp-props="{"201341983":0,"335559739":0,"335559740":240}"> </span></p> <p><span data-contrast="auto">BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely.</span> <br><span data-contrast="auto">At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.</span> <span data-ccp-props="{"201341983":0,"335559739":0,"335559740":240}"> </span></p> <p><span data-contrast="auto">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!</span> <span data-ccp-props="{"201341983":0,"335559739":0,"335559740":240}"> </span></p> <p> </p> <p> </p> <p><strong>What you'll do:</strong></p> <ul> <li>Drive BEES AI’s mission, vision, and ML strategy into concrete technical roadmaps and align architecture and standards with long-term goals.</li> <li>Own technical end-to-end delivery of ML pipelines—from architecture and data/feature pipelines through model development, deployment, and production scaling—in partnership with data, platform, and product teams.</li> <li>Design and standardize the ML model development framework (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.</li> <li>Build and implement observability across the model lifecycle (training and production) via monitoring, logging, and alerting for performance, quality, and drift.</li> <li>Ensure optimized deployments that minimize cost and maximize quality (e.g. scaling, resource allocation, inference optimization) while meeting SLAs.</li> </ul> <p> </p> <p><strong>What you'll need:</strong></p> <ul> <li>Bachelor's degree in computer science, engineering, mathematics, or any quantitative field. A master's degree is a plus.</li> <li>Relevant experience with ML platform architecture (e.g. feature stores, model registries, training and inference pipelines).</li> <li>Systems thinking (scalability, reliability, latency, cost)</li> <li>Software design patterns for building maintainable, production-ready ML systems.</li> <li>Python, PySpark and SQL. Java, Rust and NodeJS is a plus</li> <li>Kubernetes, Databricks, Terraform, Azure Devops (Git), Azure Cloud, ML Frameworks (BentoML, Kedro, Seldon, KServe, Triton Inference Server, etc.) and ML Libraries (Scikit Learn, Pytorch, Tensor Flow, ONNX, etc.)</li> </ul> <p> </p> <p><strong>More about you:</strong></p> <ul> <li>You´re motivated by building up world class solutions that impacts millions of users all over the world;</li> <li>You´re creative when solving problems and is continuously seeking improvements for processes and solutions;</li> <li>You can communicate clearly with your team and the company to identify issues and demands;</li> <li>You have the autonomy to recognize the current and new priorities, evaluating the impact of their outcomes in the final result.</li> </ul> <p> </p> <p><strong>What We Offer:</strong></p> <ul> <li>Performance based bonus*</li> <li>Attend