Agent Orchestration Engineer

Action-Oriented Agents

Key Responsibilities

• Agentic Architecture Design:

  Develop multi-agent workflows using patterns like Sequential, Parallel, and Manager-Worker to solve complex enterprise problems.

• Orchestration Framework Implementation:

  Build and maintain agent logic using frameworks such as

  LangGraph, CrewAI, AutoGen, or Semantic Kernel

  .

• System Integration:

  Connect AI agents to enterprise systems (SAP, Salesforce, EHRs) via robust tool-calling and API integrations.

• Memory & Context Management:

  Design persistent memory layers so agents can remember user preferences and past interactions across long sessions.

• Behavioral Guardrails:

  Implement safety protocols (e.g., NeMo Guardrails) to ensure agents operate within legal, ethical, and operational boundaries.

• State Machine Management:

  Handle "Human-in-the-loop" (HITL) checkpoints for high-risk autonomous actions (e.g., approving a $100k loan).

• Performance Monitoring:

  Use observability tools like

  LangSmith or Arize Phoenix

  to debug agent "hallucinations" and optimize token consumption.

Required Technical Skills

• Languages:

  Advanced proficiency in

  Python

  (asyncio, Pydantic) and experience with TypeScript/Node.js.

• Frameworks:

  Hands-on experience with

  LangChain/LangGraph

  ,

  CrewAI

  , or

  Microsoft AutoGen

  .

• Models:

  Deep understanding of LLM reasoning capabilities (GPT-4o, Claude 3.5 Sonnet, Llama 3) and how to leverage

  Function Calling

  and

  Structured Outputs

  .

• Data & Memory:

  Experience with Vector Databases (Pinecone, Weaviate, Milvus) and Graph Databases for knowledge retrieval.

• Deployment:

  Familiarity with containerization (

  Docker/K8s

  ) and cloud-native AI services (AWS Bedrock Agents, Azure AI Studio).