AI Engineer Intern

InternHunt; Bengaluru; On-site; INR 167,000 - 391,000; Part time

Job summary

InternHunt is seeking an AI Engineer Intern to design, build, and evaluate agentic AI applications using LLMs. You will develop tool-enabled agents, harnesses, and MCP integrations to handle multi-step business workflows.

The role emphasizes reliability, observability, and measurable AI performance, with hands-on work in Python, APIs, and enterprise data, contributing to real-world automation projects.

Qualifications

• Strong Python fundamentals, OOP, and asynchronous programming.
• Hands-on exposure to LangGraph, DeepAgents, or comparable agent frameworks.
• Understanding of agent execution loops, middleware, and tool integration.
• Knowledge of MCP architecture and building or integrating MCP servers/tools.
• Familiarity with tracing and monitoring tools for LLM/agent workflows.
• Understanding of prompts, embeddings, structured outputs, function calling, and RAG.
• Git, debugging, unit testing, and basic database knowledge.

Responsibilities

• Agentic AI & Agent Harness Development and tooling.
• Design AI agents using LangGraph, LangChain, and DeepAgents.
• Build and customize agent harnesses for planning and multi-step workflows.
• Implement middleware for retries, errors, and context management.
• Manage agent memory, state, checkpointing, and persistence.
• Develop reusable tools, sub-agents, and orchestration workflows.
• Integrate MCP servers/clients to connect agents with external systems.
• Create tools for APIs, databases, and enterprise apps.
• Implement structured tool calls, input validation, and secure execution.
• Understand tool discovery, permissions, and integration patterns.
• AI Observability & Monitoring with tracing frameworks.
• Capture traces, token usage, latency, and failures for analysis.
• Debug agent execution paths to improve reliability and performance.
• Support monitoring and optimization of AI app quality and costs.
• LLM Evaluations: design evaluation datasets and frameworks.
• Measure correctness, precision, recall, consistency, and hallucinations.
• Develop regression tests for prompts, models, and workflows.
• Experiment with LLM-as-a-Judge and human feedback mechanisms.
• Generative AI: develop Python-based LLM-powered apps and APIs.
• Implement prompt engineering, function calling, and RAG.
• Build APIs using FastAPI and back-end services.
• Handle structured/unstructured enterprise data for real-world use cases.

Skills

Python fundamentals

OOP

Asynchronous programming

REST APIs

JSON

LangGraph

DeepAgents

agent orchestration

MCP architecture

Observability

Evals

LLMs

Git

Debugging

Unit testing

Databases

Education

B.E./B.Tech./M.Tech./MCA in CS/AI/DS/IT

Tools

LangChain

DeepAgents

FastAPI

Pydantic

PostgreSQL

MongoDB

Docker

OpenTelemetry

MLflow

LangSmith

Langfuse

Job description

Job Description – AI Engineer Intern (Agentic AI)

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.
• 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.