Senior Machine Learning Engineer

AB InBev | Growth Group

Campinas

Presencial

BRL 240 000 - 360 000

Tempo integral

14 dias+

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Vantagens oferecidas por esta oferta de emprego

Performance-based bonus
Attendance bonus
Casual office and dress code
Days off
Health, dental, and life insurance
Discounts on Ambev products
School materials assurance
Language and training platforms

Resumo da oferta

AB InBev Growth Group is seeking an experienced machine learning platform architect to drive BEES AI’s mission and translate strategy into practical, scalable ML pipelines. You will own the end-to-end delivery of data and ML workflows, partnering with data, platform, and product teams to design robust, production-ready systems.

The role focuses on building standardized frameworks for model development, ensuring observability, and delivering cost-efficient deployments that meet SLAs while scaling

Qualificações

  • Bachelor's degree in computer science, engineering, mathematics, or related quantitative field required; master's degree a plus.
  • Experience with ML platform architecture (feature stores, model registries, training and inference pipelines).
  • Strong systems thinking covering scalability, reliability, latency, and cost.
  • Experience applying software design patterns to production ML systems.
  • Proficiency in Python, PySpark and SQL; knowledge of Java, Rust or NodeJS is a plus.
  • Experience with Kubernetes, Databricks, Terraform, Azure DevOps, Azure Cloud and ML frameworks/libraries listed.

Responsabilidades

  • Drive BEES AI’s mission, vision, and ML strategy into concrete technical roadmaps.
  • Own end-to-end delivery of ML pipelines from architecture to deployment and production scaling.
  • Design and standardize ML model development framework for consistency and reuse.
  • Build observability across the ML lifecycle with monitoring and alerting for performance and drift.
  • Ensure optimized deployments that minimize cost while meeting SLAs.

Conhecimentos

ML platform architecture
Systems thinking
Software design patterns
Python
PySpark
SQL
Java
Rust
NodeJS

Formação académica

Bachelor's degree in computer science, engineering, mathematics, or any quantitative field
Master's degree (a plus)

Ferramentas

Kubernetes
Databricks
Terraform
Azure DevOps
Azure Cloud
BentoML
Kedro
Seldon
KServe
Triton Inference Server
Scikit-Learn
PyTorch
TensorFlow
ONNX

Descrição da oferta de emprego

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.

What you'll do:
  • Drive BEES AI’s mission, vision, and ML strategy into concrete technical roadmaps and align architecture and standards with long-term goals.
  • 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.
  • Design and standardize the ML model development framework (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
  • Build and implement observability across the model lifecycle (training and production) via monitoring, logging, and alerting for performance, quality, and drift.
  • Ensure optimized deployments that minimize cost and maximize quality (e.g. scaling, resource allocation, inference optimization) while meeting SLAs.
What you'll need:
  • Bachelor's degree in computer science, engineering, mathematics, or any quantitative field. A master's degree is a plus.
  • Relevant experience with ML platform architecture (e.g. feature stores, model registries, training and inference pipelines).
  • Systems thinking (scalability, reliability, latency, cost)
  • Software design patterns for building maintainable, production-ready ML systems.
  • Python, PySpark and SQL. Java, Rust and NodeJS is a plus
  • 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.)
More about you:
  • You´re motivated by building up world class solutions that impacts millions of users all over the world;
  • You´re creative when solving problems and is continuously seeking improvements for processes and solutions;
  • You can communicate clearly with your team and the company to identify issues and demands;
  • You have the autonomy to recognize the current and new priorities, evaluating the impact of their outcomes in the final result.
What We Offer:
  • Performance based bonus*
  • Attendance Bonus*
  • Casual office and dress code
  • Days off*
  • Health, dental, and life insurance
  • Discounts on Ambev products*
  • School materials assurance
  • Language and training platforms
Equal Opportunity & Aff. Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Aff. 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.

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