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 1. 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. 2. 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. 3. 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. 4. 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. 5. Generative…