Role Overview :

The Agentic AI Engineer will build and implement intelligent agent workflows using modern agent orchestration frameworks. The role focuses on building scalable AI agents, implementing memory architectures, and enabling agent-to-agent communication for complex enterprise workflows.

Key Responsibilities :

- Design and implement LangGraph-based agent workflows, including state machines, parallel execution, human-in-the-loop workflows, and interrupt handling.

- Develop agent-to-agent (A2A) communication protocols and routing logic for coordinated multi-agent systems.

- Integrate external tools, APIs, and Model Context Protocol (MCP) endpoints into agent workflows.

- Design and implement multi-tier memory architecture including :

1. Session state

2. Working memory

3. Long-term episodic memory

- Build memory read/write APIs for runtime agent interactions.

- Implement TTL policies, pruning strategies, and memory relevance scoring.

- Ensure reliable and scalable agent orchestration and runtime performance.

Required Skills & Experience :

- Production experience with LangGraph or LangChain

- Strong Python expertise (asyncio, FastAPI, pytest)

- Hands-on experience with AWS Bedrock AgentCore or similar AI runtimes

- Experience with LLM prompting, function calling, and tool integration

- Strong knowledge of stateful workflows and finite state machines

- Experience with agent memory systems such as Mem0, Zep, or LangGraph Checkpointers

- Experience with Redis or DynamoDB for low-latency state management

- Exposure to CrewAI, AutoGen, or Semantic Kernel

- Experience building AI solutions for contact center or conversational systems is preferred