AI Engineer | BEES Personalization

About us

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.

 

About the job

We are looking for a highly skilled AI Engineer to join our team and lead the design and implementation of complex AI agents capable of reasoning, planning, and collaborating with humans and other systems. This role goes beyond building simple chatbots: you will be responsible for creating multi-agent architectures, integrating advanced LLMs, and ensuring robustness, scalability, and security in production environments.

You will work closely with product managers, data engineers, and security specialists to develop agent-based systems that handle real-world complexity at scale.

 

What you'll do:

• Design, develop, and deploy multi-agent AI systems for reasoning, planning, and task execution.
• Implement RAG (Retrieval-Augmented Generation) pipelines, using orchestration tools and frameworks 
  (LangChain, LangGraph, Azure AI Foundry).
• Optimize model serving for low-latency, high-throughput inference using frameworks like KServe, 
  Triton Inference Server, or Ray Serve.
• Build observability and evaluation frameworks to monitor agent reasoning, success rates, and failure cases.
• Collaborate with ML and data engineers to integrate structured and unstructured data sources into agent workflows.
• Apply security and alignment techniques (guardrails, prompt injection prevention, red-teaming) 
  to ensure robust behavior.
• Work in a CI/CD environment (Azure DevOps, GitHub Actions, ArgoCD) for rapid iteration and reliable deployment.
• Participate in architectural decisions involving distributed systems, GPU usage, 
  caching strategies, and memory management.

 

What you'll need:

• Strong proficiency in Python (for prototyping) and C++/Go (for performance-critical components).
• Proven experience with LLMs (OpenAI, Anthropic, Llama, Mistral, or similar).
• Practical