[Hiring] AI Engineer @AB InBev | Growth Group

Role Description

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.

Qualifications

• 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).

Requirements

• 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.

Benefits

• Performance based bonus*
• Attendance Bonus*
• Private pension plan
• Meal Allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance
• Medicines discounts
• WellHub partnership
• Childcare subsidies
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship*
• School materials assurance
• Language and training platforms
• Transport allowance