{"id":14086,"url":"https://alion.io/job/abinbev-intermediate-machine-learning-engineer-bees-data","title":"Mid Level Machine Learning Engineer","company":{"id":3746,"name":"Abinbev","domain":"abinbev.com","url":"https://alion.io/company/abinbev","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":88,"open_postings":8,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":46,"computed_at":"2026-10-07T05:47:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Campinas, Brazil"],"countries":["BR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Java","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"SQL","optional":false},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"BentoML","optional":true},{"name":"Databricks","optional":true},{"name":"Git","optional":true},{"name":"Kedro","optional":true},{"name":"KServe","optional":true},{"name":"Kubernetes","optional":true},{"name":"ONNX","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Seldon Core","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true},{"name":"Terraform","optional":true},{"name":"Triton Inference Server","optional":true}],"status":"live","first_seen_at":"2026-07-22T22:01:35Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-07T23:38:30Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":77},"description":"About us\nAB 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.\nAbout AB InBev Growth Group\nCreated 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.\nIn 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.\nWe are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.\nWhat you'll do:\nImplement and extend ML platform capabilities (training jobs, inference services, batch and online serving) following team architecture, standards, and best practices.\nDevelop and maintain components of the ML model development workflow (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.\nBuild 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.\nSupport optimized model deployments (scaling, resource allocation, inference tuning) to meet cost, quality, and SLA targets.\nTroubleshoot pipeline and serving issues, document solutions, and share learnings with the team.\nWhat you'll need:\nBachelor’s degree in computer science, engineering, mathematics, or another quantitative field.\nPractical experience with ML platform components (e.g., feature pipelines, model registries, training and inference workflows).\nSolid software engineering fundamentals: clean code, testing, CI/CD, and maintainable system design.\nPython, PySpark, and SQL. Exposure to Java is a plus.\nExperience 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.).\nComfort collaborating across teams, communicating technical tradeoffs clearly, and learning from senior engineers on architecture and platform decisions.\nWhat We Offer:\nPerformance based bonus*\nAttendance Bonus* \nPrivate pension plan\nMeal Allowance\nCasual office and dress code\nDays off*\nHealth, dental, and life insurance\nMedicines discounts\nWellHub partnership\nChildcare subsidies\nDiscounts on Ambev products*\nClube Ben partnership\nScholarship*\nSchool materials assurance\nLanguage and training platforms\nTransport allowance\n*Rules applied\nEqual Opportunity & Affirmative Action:\nAB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.\nThe following fields are optional, but anticipate the information for your registration*.\nRemember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. 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