Data Science Intern -Building AI Agents at analytos.ai 🎯 About the Role We’re looking for a passionate Data Science Intern who loves building AI-powered agents and automation systems. You’ll work closely with our AI and engineering teams to design, build, and deploy intelligent agentic workflows that automate real-world business processes. This is an opportunity to work hands-on with the latest frameworks like LangChain and LangGraph, while learning to build production-grade AI applications from scratch. 🧠 What You’ll Do Build and test AI agents that automate multi-step workflows and decision-making processes. Use frameworks such as LangChain and LangGraph to design reasoning and orchestration flows. Implement retrieval-augmented generation (RAG), multi-agent communication (A2A), and tool-augmented reasoning (MCP). Work on data ingestion, preprocessing, and vector search pipelines for LLM integration. Develop Python-based backends using FastAPI for API exposure of agents. Build minimal frontends with React + TypeScript to visualize or control agent workflows. Collaborate with the data science and full-stack teams to deploy working agentic prototypes. ✅ What We’re Looking For Good understanding of Machine Learning, NLP, and LLM architectures. Hands-on experience with LangChain, LangGraph, LlamaIndex, or similar agent frameworks. Exposure to prompt engineering and multi-agent reasoning patterns. Proficiency in Python (FastAPI preferred) and basic web development (React/TypeScript is a plus). Interest in building AI-native workflows that integrate APIs, tools, and reasoning models. Strong problem-solving skills and curiosity to explore new AI frameworks. 💡 Nice to Have Experience with vector databases like FAISS, Chroma, or Pinecone. Basic understanding of RAG, MCP, A2A, or ReAct frameworks. Exposure to OpenAI, Anthropic Claude, or local LLM integrations. Previous experience working on AI assistants, chatbots, or autonomous workflows. 🎁 What You’ll Get Mentorship from senior AI engineers and architects. Hands-on experience building end-to-end AI products, not just models. Exposure to real client use cases in automation and enterprise AI. Potential to transition to a full-time role based on performance.