AI Engineer Intern

**Job Description – AI Engineer Intern (Agentic AI)** **Role:** AI Engineer Intern – Agentic AI & LLM Engineering **Department:** AI Engineering **Location:** Bengaluru **Employment Type:** Internship **About The Role** We are looking for a technically strong and passionate **AI Engineer Intern** with hands-on experience building Agentic AI applications using Large Language Models (LLMs). The ideal candidate should have practical exposure to **DeepAgents, LangGraph, Agent Harness development, Model Context Protocol (MCP), AI Observability, and LLM Evaluations (Evals)** through academic projects, personal projects, internships, or open-source contributions. You will work on designing, developing, evaluating, and deploying AI agents capable of performing complex, multi-step business workflows by interacting with tools, APIs, databases, and enterprise systems. We are looking for candidates who go beyond basic chatbot development and are interested in building reliable, tool-enabled, and measurable AI applications. Key Responsibilities * Agentic AI & Agent Harness Development * Design and develop AI agents using LangGraph, LangChain, and DeepAgents. * Build and customize agent harnesses to support planning, tool execution, context management, and multi-step workflows. * Implement agent middleware for tool retries, error handling, human-in-the-loop approvals, and context management. * Work with agent memory, state management, checkpointing, and persistent execution. * Develop reusable tools, sub-agents, and agent orchestration workflows. * Model Context Protocol (MCP) & Tool Integration * Develop and integrate MCP servers and clients to connect AI agents with external systems. * Build reusable tools for interacting with APIs, databases, and enterprise applications. * Implement structured tool calling, input validation, error handling, and secure tool execution. * Understand tool discovery, tool permissions, and integration patterns. * AI Observability & Monitoring * Implement end-to-end tracing and monitoring for LLM and agent-based applications. * Work with observability frameworks such as MLflow, LangSmith, Langfuse, or OpenTelemetry. * Capture and analyze agent execution traces, tool calls, model responses, token consumption, latency, and failures. * Debug agent execution paths and identify reliability and performance issues. * Support monitoring and optimization of AI application quality and inference costs. * LLM Evaluations (Evals) & Reliability * Design and implement evaluation frameworks for AI agents and LLM applications. * Create evaluation datasets with ground-truth examples and expected outputs. * Implement evaluations for tool selection, tool execution, structured outputs, and end-to-end agent decisions. * Measure correctness, precision, recall, consistency, hallucinations, and task completion. * Develop automated regression tests to evaluate changes in prompts, models, and agent workflows. * Experiment with LLM-as-a-Judge and human feedback mechanisms. * Generative AI & Application Development * Develop LLM-powered applications using Python and model APIs. * Implement prompt engineering, structured outputs, function calling, and Retrieval-Augmented Generation (RAG). * Build APIs and backend services using FastAPI or similar frameworks. * Work with structured and unstructured enterprise data, including documents, emails, PDFs, and databases. * Collaborate with engineering teams to develop and deploy AI solutions for real-world business use cases. **Required Technical Skills** * Programming: Strong Python fundamentals, OOP, asynchronous programming, REST APIs, and JSON. * Agentic AI: Hands-on exposure to LangGraph, DeepAgents, or comparable agent frameworks, including agent orchestration and tool calling. * Agent Harness: Understanding of agent execution loops, middleware, tool integration, state management, and error handling. * MCP: Understanding of MCP architecture and experience building or integrating MCP servers and tools. * Observability: Familiarity with instrumenting and debugging LLM or agent workflows using tracing and monitoring tools. * Evals: Understanding of evaluation datasets, correctness metrics, and methods for evaluating LLM and agent outputs. * LLMs: Knowledge of prompts, embeddings, structured outputs, function calling, and RAG. * Software Engineering: Git, debugging, unit testing, and basic database knowledge. Good to Have * Experience with Azure OpenAI, OpenAI APIs, Anthropic, or open-source LLMs. * Familiarity with MLflow, LangSmith, Langfuse, or OpenTelemetry. * Experience with Pydantic, FastAPI, PostgreSQL, MongoDB, or vector databases. * Knowledge of Docker, cloud deployment, and CI/CD pipelines. * Exposure to multi-agent architectures, human-in-the-loop workflows, and persistent agent memory. * Familiarity with document intelligence, information extraction, or enterprise workflow automation. **Educational Qualifications** * Currently pursuing or recently completed B.E./B.Tech/M.Tech/MCA in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related discipline. * Strong programming fundamentals and demonstrated interest in AI engineering. * Academic projects, open-source contributions, hackathons, or independent AI projects are an advantage. Ideal Candidate **We Are Looking For Someone Who** * Has explored building AI agents beyond basic prompt-based applications. * Understands how agents interact with tools, manage state, and execute workflows. * Thinks about AI reliability, evaluations, and observability. * Can debug agent workflows and identify opportunities for improvement. * Enjoys working on real-world engineering challenges. * Is proactive, technically curious, and comfortable exploring rapidly evolving AI technologies. * Can explain the architecture, implementation, and technical decisions behind their projects. * Our ideal candidate is a hands-on AI builder who can design an agent, connect it to real tools, evaluate its decisions, observe its execution, and continuously improve its reliability.