Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen.
Develop and deploy multi-agent systems using MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols to facilitate communication, tool usage, and collaborative task solving.
Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention.
Fine-tune Small Language Models (SLMs) for specific domains and optimize them for edge device performance, including ONNX, GGML, or Ollama.
Develop evaluation frameworks to test agent reliability, safety, and performance, moving from prototype to production, including ReAct loops and human-in-the-loop.

Requirements

5+ years of experience in software development
2+ years of experience with GenAI, LLMs, and agentic workflows
Experience with LangChain, LangGraph, AutoGen, or LlamaIndex
Experience with Model Context Protocol (MCP) for tool integration and A2A for agent-to-agent collaboration
Experience in RAG architecture and Knowledge Graphs, including Neo4j or NebulaGraph
Experience fine-tuning LLMs or SLMs using Hugging Face, PEFT, or LoRA
Knowledge of modern software design patterns, including microservice design or edge computing
Ability to obtain a Secret clearance
Bachelor’s degree in a Computer Science or AI field

Benefits

health, life, disability, financial, and retirement benefits
paid leave
professional development
tuition assistance
work-life programs
dependent care
recognition awards for exceptional performance

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

LangChainLangGraphAutoGenModel Context Protocol (MCP)Agent-to-Agent (A2A)RAG architectureKnowledge Graphsfine-tuning LLMsHugging Facemicroservice design

Soft Skills

reasoningplanningcollaborative task solvingevaluation frameworksagent reliabilitysafetyperformance optimization

Certifications

Secret clearance