Lead AI Engineer

Role Overview

We are seeking a Lead AI Engineer (Agentic Systems) with strong expertise in software engineering, data engineering, machine learning, artificial intelligence technologies including large language models (LLMs), autonomous agent systems development using frameworks like LangGraph/CrewAI/AutoGen.

Key Responsibilities:

• Multi-Agent System Architecture: Design and build production-grade agentic workflows enabling intelligent applications capable of reasoning, planning, executing complex tasks autonomously. Implement Agent-to-Agent (A2A) communication protocols for collaborative multi-agent environments.
  
  
• State Management & Orchestration: Engineer robust control flows supporting non-deterministic agent operations including message passing, memory persistence, interruptible state handling.
• MCP Implementation: Standardize Model Context Protocol (MCP) interfaces between agents and data sources ensuring modularity/scalability.
  
  
• Data Engineering Integration: Build high-throughput ingestion pipelines transforming operational signals into structured context for real-time agent access. Optimize vector store performance for low-latency retrieval operations.
• LLMOps & Observability: Implement comprehensive tracing using tools like Langfuse, monitor token usage, debug latency issues. Establish evaluation pipelines measuring hallucination rates/drift.
  
  
• Safety Frameworks: Design hybrid execution modes ranging from Human-in-the-Loop (HITL) to fully autonomous operations including break-glass mechanisms.

Tech Stack Requirements:

You must demonstrate advanced proficiency in Python for systems engineering combined with expertise in vector databases (Pinecone/Weaviate/Qdrant), cloud infrastructure orchestration via Kubernetes/Docker on AWS/GCP/Azure, knowledge of RAG/embeddings mechanics.