Description

You will build and maintain agentic AI pipelines using LangGraph and RAG systems for enterprise knowledge ingestion.

This role is on-site.

Responsibilities

• Develop and extend multi-agent orchestration systems using LangGraph for complex task execution.
• Build end-to-end RAG pipelines including document ingestion (PDF, TXT, CSV, Markdown), chunking, vector embeddings, and semantic search.
• Integrate with internal GenAI LLM endpoints for natural language understanding, summarization, and agent reasoning.
• Implement MCP Tool Server capabilities to advertise, register, and dispatch tool calls for dynamic agent invocation.
• Design and optimize system prompts, directives, and output parsing to ensure production reliability.

Required Skills

• 5+ years in AI/ML engineering or data engineering with hands-on LLM/GenAI application development.
• Strong Python proficiency including Flask, pandas, and async/streaming patterns.
• Experience with data processing libraries such as pdfplumber, python-docx, and openpyxl.
• Hands-on experience with LangGraph, LangChain, or similar agent orchestration frameworks.
• Deep understanding of tool calling patterns, multi-step reasoning, and state management.
• Proven experience building RAG pipelines with vector storage solutions like ChromaDB, Pinecone, Weaviate, or FAISS.
• Ability to design robust system prompts, execution directives, and guardrails for production LLM applications.
• Bachelor's degree in Computer Science, Engineering, or related field.

Key Skills

Education

Bachelor's degree