AI Engineer - Bees Personalization

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.

• →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).

• 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.