- *Agentic AI \& GenAI Engineer Intern** AI Platform · SDLC Intelligence · Multi\-Agent Systems Internship \| 6–12 months \| 6\+ months experience required
We're building a fully agentic, LLM\-native platform that rewires how software gets designed, tested, and shipped — from requirements to release. Think autonomous agents that read tickets, generate code, catch defects, and trigger CI/CD pipelines — all orchestrated through a system you help design.
This isn't a "run some notebooks" internship. You'll work hands\-on with multi\-agent frameworks, MCP servers, RAG pipelines, and LLM tool\-use patterns — and your work ships into real systems. If you've already spent 6\+ months tinkering with LLMs, building backend services, or wiring up agentic workflows, you'll hit the ground running.
- *What You'll Actually Do**
Agentic Systems
- Build and iterate on multi\-agent orchestration flows using LangChain, LangGraph, or AutoGen
- Design and expose MCP (Model Context Protocol) servers to give agents structured access to tools, repos, and APIs
- Implement agent memory, reflection loops, and tool\-use patterns (ReAct, Plan\-and\-Execute)
LLM \& GenAI Engineering
- Work on LLM use cases across the SDLC: requirement parsing, test case generation, code review, defect triage
- Build and tune RAG pipelines over codebases, logs, and defect databases using vector stores
- Experiment with prompt strategies — chain\-of\-thought, few\-shot, structured output, tool\-calling
Backend \& API Integration
- Build FastAPI/REST backend services that expose agent capabilities to product surfaces
- Connect LLM workflows into CI/CD pipelines (GitHub Actions, Jenkins) for automated code analysis and testing
- Integrate with coding tools — IDE plugins, PR bots, CLI assistants
Evaluation \& Reliability
- Design evaluation frameworks for agent outputs — accuracy, hallucination rate, tool\-call fidelity
- Build observability into pipelines: tracing, structured logging, drift detection
- Own experiments end\-to\-end: hypothesis → implementation → measurement → ship
- *What You Need (Must\-Have)**
- Python 3\.x — solid, not just scripting
- Hands\-on experience with LLMs and prompt engineering
- Understanding of RAG pipelines and vector search
- REST API development and integration
- Git and version control
- Basic ML concepts (classification, evaluation metrics)
- Familiarity with agentic workflows and tool\-use patterns
- LangChain, LangGraph, or similar orchestration frameworks
- MCP server design and integration
- Vector databases (Chroma, Pinecone, Qdrant)
- FastAPI or Flask for backend services
- CI/CD pipelines (GitHub Actions, Jenkins)
- Cloud platforms (Azure, AWS, or GCP)
- Docker basics
- TypeScript fundamentals
- AutoGen or CrewAI experience
- LLM fine\-tuning (LoRA, PEFT)
- AI coding tools (Cursor, Copilot, Cline)
- SDLC or QA process exposure
- OpenTelemetry or observability tooling
- *The Baseline We Expect**
6\+ months of hands\-on experience with at least one of: LLM application development, backend API engineering, or agentic/GenAI systems. This could be from a previous internship, freelance project, open\-source contribution, or a personal project you can walk us through. We care about what you've actually built — not where you studied.
Pay: ₹20,000\.00 \- ₹40,000\.00 per month
Work Location: In person