Description:
We are seeking a LangChain + LangGraph Intern to help us prototype and build intelligent agentic systems powered by large language models. If you've already been experimenting with LangGraph’s stateful agents and LangChain’s toolchains, you’ll thrive here.
You'll be working on real use cases like autonomous agents, document Q&A pipelines, retrieval-augmented generation (RAG), memory-based flows, and more.
This role is technical, hands-on, and creative ideal for builders and AI enthusiasts who want to deepen their skills in composable AI systems.
- What You’ll Work OnBuild multi-step LangGraph agents with tools, memory, and condition-based state transitions
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- Create modular LangChain chains for RAG, Q&A, classification, and summarization tasks
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- Implement context-aware workflows that combine retrieval, generation, and action
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- Integrate APIs, embeddings, vector databases (e.g., Pinecone, FAISS) with LangChain
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- Design and test stateful conversational flows with persistent memory and tool usage
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- Collaborate with the core AI team on designing robust, scalable graph architectures
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- Requirements
- Hands-on experience with LangChain (tools, chains, retrievers, memory modules)
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- Working knowledge of LangGraph, including:
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- Graph states and edges
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- Agent executors with tools
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- Conditional routing and control flow
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- Proficient in Python
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- Familiarity with LLM APIs (OpenAI, Claude, Cohere, etc.)
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- Good understanding of vector search, embeddings, and context injection
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- Ability to work independently and communicate clearly in a hybrid/async setup
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- Bonus Skills (Not Required)
- Experience with streamlit or Gradio for UI prototyping
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- Familiarity with fastAPI for wrapping agents into APIs
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- Contributions to open-source LangChain/LangGraph projects
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- What You’ll Get
- Opportunity to work on cutting-edge LLM agent systems using LangGraph
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- Mentorship from AI engineers working on production LLM applications
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- Flexible working hours (we respect your time!)
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- Potential for full-time placement