AI Engineer Internship

Skills this role screens for

About the role

Discover Dollar is hiring for the role of AI Engineer Intern!

The work

Responsibilities of the Intern

• 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.
• 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.
• 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.

Who they want

Requirements

• 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.
• 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.