AI Engineer - Bees Personalization
Quick Summary
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).
Strong proficiency in Python (for prototyping) and C++/Go (for performance-critical components). Proven experience with LLMs (OpenAI, Anthropic, Llama, Mistral, or similar).
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.
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.
About the Role
~1 min readWe 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.
Responsibilities
~1 min read- →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.
- Strong proficiency in Python (for prototyping) and C++/Go (for performance-critical components).
- Proven experience with LLMs (OpenAI, Anthropic, Llama, Mistral, or similar).
- Practical experience with agent orchestration frameworks (LangChain, LangGraph, Semantic Kernel).
- Deep understanding of search and retrieval systems (vector databases like Pinecone, Weaviate, FAISS, Milvus).
- Knowledge of distributed systems and experience deploying ML models in Kubernetes/AKS/EKS/GKE environments.
- Familiarity with ONNX Runtime, TensorRT, or DeepSpeed for inference optimization.
- Experience with public clouds (Azure, AWS, or GCP) and infrastructure as code (Terraform, CloudFormation, or Bicep).
- Understanding of AI security (prompt injection, data leakage, adversarial testing).
Nice to Have
~1 min read- Experience with multi-agent simulations (AutoGen, CrewAI, OpenAI Swarm).
- Background in red-teaming and offensive security for AI models.
- Knowledge of graph databases (Neo4j, ArangoDB) for agent memory and reasoning.
- Publications, research contributions, or involvement in open-source projects related to LLM or agent frameworks.
What We Offer
~1 min readAB 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.
The following fields are optional, but anticipate the information for your registration*.
Remember: 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. We want to contribute to changing this reality by being an inclusive company.
For more information: www.abinbev.com
Location & Eligibility
Listing Details
- Posted
- May 7, 2026
- First seen
- May 7, 2026
- Last seen
- May 8, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 67%
- Scored at
- May 7, 2026
Signal breakdown
Please let Abinbev know you found this job on Jobera.
3 other jobs at Abinbev
View all →Explore open roles at Abinbev.
Similar Machine Learning Engineer jobs
View all →Stay ahead of the market
Get the latest job openings, salary trends, and hiring insights delivered to your inbox every week.
No spam. Unsubscribe at any time.