AI Engineering Intern

About the Role

As an AI Engineering Intern, you'll work directly with our core engineering team to design, build, and evaluate AI-powered systems that solve real problems at scale.

Responsibilities

Design and develop AI agents and coding agents using leading LLMs, integrating tool use, memory, and multi-step reasoning.

• Build and optimize Retrieval-Augmented Generation (RAG) pipelines — from chunking and embeddings to vector stores and reranking.
• Implement Model Context Protocol (MCP) integrations and custom tool harnesses to extend LLM capabilities into real-world systems.
• Craft and systematically refine prompts (zero-shot, few-shot, chain-of-thought), documenting what works and why.
• Build AI workflows that orchestrate multiple models, tools, and data sources into end-to-end applications.
• Construct evaluation (eval) frameworks to benchmark model accuracy, reliability, and regression.
• Instrument AI systems with observability tooling — traces, token usage, latency, failure modes, and cost tracking.
• Contribute to generative AI applications across text, code, and multimodal contexts.

What You'll Need

• Currently pursuing or recently completed a B.Tech / M.Tech / MS / PhD in Computer Science, AI/ML, or a related field.
• Strong Python programming skills, with clean, readable, production-aware code.
• Genuine curiosity about how LLMs work, and a habit of experimenting independently.
• Familiar with at least one LLM orchestration framework (LangChain, LlamaIndex, CrewAI, Haystack, or similar).
• Exposure to vector databases (Pinecone, Weaviate, Chroma, pgvector) and embedding models is a plus.
• Strong written communication — able to document findings and share learnings clearly.

Resume Tip

Highlight your experience with Python, LLMs, and AI/ML concepts, as well as any relevant projects or research you've worked on.