Discover Dollar AI Engineer Intern 2026: Agentic AI Internship in Bengaluru
If you have been experimenting with large language models and want to move beyond simple chatbot projects, the AI Engineer Intern – Agentic AI & LLM Engineering opportunity at Discover Dollar may be worth exploring.
The role focuses on developing AI agents that can interact with tools, call APIs, manage context, execute multi-step tasks, and produce measurable results. It also places unusual emphasis on engineering practices that are often missing from beginner GenAI projects: observability, automated evaluations, error handling, and reliability.
At a glance
| Job detail | Information |
|---|---|
| Company | Discover Dollar |
| Role | AI Engineer Intern – Agentic AI & LLM Engineering |
| Department | AI Engineering |
| Location | Bengaluru, Karnataka |
| Experience | 0–1 years, according to the supplied listing |
| Employment type | Internship in the detailed description; confirm the full-time label |
| Core programming language | Python |
| Main areas | Agentic AI, LLM engineering, MCP, observability and evaluations |
| Educational background | B.E./B.Tech/M.Tech/MCA in CS, AI, Data Science, IT or a related discipline |
| Salary or stipend | Not specified in the supplied job description |
| Application deadline | Not specified in the supplied job description |
What will you actually build in this role?
The position centres on AI applications that do more than generate text. An agent may need to decide which tool to use, call an external service, inspect the result, recover from an error, and continue until a task is completed.
The responsibilities fall into five connected areas.
1. Agent development and orchestration
The role mentions LangGraph, LangChain and DeepAgents. These frameworks can help structure agent workflows, manage state, coordinate tool calls, and organize multi-step execution.
Interns may contribute to agent harnesses—the software surrounding an AI model that manages how an agent runs. This can include retries, error handling, context management, memory, checkpoints, and human approval steps.
2. Connecting agents to external systems with MCP
The Model Context Protocol (MCP) is relevant when an AI application needs a structured way to discover and use tools or connect with external systems.
The job description includes developing or integrating MCP servers and clients, building reusable tools, validating inputs, handling errors, and applying appropriate permissions. Understanding how to connect an agent to a real API or database is therefore more relevant than demonstrating prompts alone.
3. Monitoring AI applications
An agent can fail in ways that are difficult to identify from its final answer. It might choose the wrong tool, make repeated calls, take too long, consume excessive tokens, or receive an unexpected response.
The role calls for tracing and monitoring agent execution, including tool calls, model responses, latency, token consumption, and failures. Tools mentioned include LangSmith, Langfuse, MLflow and OpenTelemetry.
4. Evaluating LLMs and agent decisions
Evaluation, or “evals,” is a major part of this position. Candidates should understand how to create test examples, define expected outputs, measure performance, and identify regressions when prompts or models change.
The description specifically mentions correctness, precision, recall, consistency, hallucinations, tool selection, and task completion. It also refers to LLM-as-a-Judge and human feedback.
5. Building complete AI applications
The internship also involves Python-based LLM applications, prompt engineering, structured outputs, function calling, retrieval-augmented generation (RAG), and backend APIs built with FastAPI or a similar framework.
This means a candidate’s project should ideally demonstrate how the model, backend, tools, and data work together—not just the model’s ability to answer questions.
The skills that matter most
The job description is extensive, so it helps to separate the central requirements from additional technologies.
Core skills to demonstrate
- Python fundamentals, object-oriented programming and asynchronous programming.
- REST APIs, JSON, debugging, Git and unit testing.
- Hands-on exposure to LangGraph, DeepAgents or comparable agent frameworks.
- Agent execution loops, tool calling, state management and error handling.
- Understanding of MCP architecture and tool integration.
- LLM concepts such as prompts, embeddings, structured outputs, function calling and RAG.
- Basic database knowledge and an understanding of how to evaluate agent outputs.
Additional skills that may strengthen your profile
- OpenAI APIs, Azure OpenAI, Anthropic or open-source LLMs.
- FastAPI, Pydantic, PostgreSQL, MongoDB and vector databases.
- LangSmith, Langfuse, MLflow or OpenTelemetry.
- Docker, cloud deployment and CI/CD.
- Multi-agent systems, persistent memory, human-in-the-loop workflows and document intelligence.
You do not need to claim expertise in every tool listed. Prioritize skills you have actually used and be ready to explain the design decisions behind your work.
A portfolio project that matches this job
One practical way to demonstrate your suitability is to build a small tool-using research or business-workflow agent.
For example, create an application that accepts a question, retrieves information from a document collection, calls a permitted external API, and produces a structured response with supporting evidence.
A useful implementation plan would be:
- Build the backend: Use Python and FastAPI to accept requests and return structured JSON.
- Create the agent workflow: Use LangGraph or a comparable framework to manage the steps, state, and tool calls.
- Connect tools: Integrate a database, document search function, or API. If appropriate, expose one of the tools through MCP.
- Add failure handling: Validate tool inputs, handle API errors, and limit retries to prevent repeated failed calls.
- Create an evaluation dataset: Prepare representative test cases with expected results and check whether the agent completes the correct task.
- Instrument execution: Record tool calls, errors, latency, and token usage using an appropriate tracing solution.
- Document the results: Explain the architecture, limitations, tests, and improvements in a README.
This is a suggested preparation project, not an assignment specified by Discover Dollar. A smaller, working application with meaningful tests is a better demonstration than a complex multi-agent system that you cannot explain.
How to prepare for a technical discussion
If you apply, practise explaining your project in terms of engineering decisions rather than simply naming frameworks.
Be ready to answer questions such as:
- What happens when an agent selects the wrong tool?
- How do you validate arguments before calling an API?
- How do you preserve state when a workflow is interrupted?
- How would you test an agent when its outputs are not always identical?
- How can you distinguish a model failure from a tool or API failure?
- How would you detect a regression after changing a prompt or model?
- What safeguards would you add before allowing an agent to perform a consequential action?
These are preparation questions inferred from the responsibilities in the listing; they are not confirmed interview questions.
Educational qualifications and experience
The description accepts candidates currently pursuing or recently completing B.E., B.Tech, M.Tech or MCA in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
Academic projects, hackathons, open-source contributions and independent AI projects are listed as advantages. The role is described as suitable for early-career applicants, with a stated experience range of 0–1 years.
The listing does not specify a minimum CGPA, a particular graduating batch, a stipend amount, or a mandatory number of months of previous experience. Check the application page for any additional screening requirements.
Frequently asked questions
Is this role suitable for freshers?
The listing gives an experience range of 0–1 years and includes students and recent graduates among the intended candidates. Practical project experience in agentic AI is particularly relevant.
Is Python required?
Yes. Strong Python fundamentals are explicitly included in the required technical skills, alongside OOP, asynchronous programming, REST APIs and JSON.
Do I need to know LangGraph and DeepAgents?
The description asks for hands-on exposure to LangGraph, DeepAgents, or comparable agent frameworks. It does not require candidates to have used every framework listed.
What does agent harness development mean?
It involves building the runtime and supporting components that help an agent execute workflows reliably, including tool integration, state and context management, retries, error handling, and approval steps.
What is MCP in this job description?
MCP stands for Model Context Protocol. The role calls for understanding its architecture and experience building or integrating MCP servers, clients, and tools.
What is the salary or stipend?
The supplied job description does not specify an amount. Treat salary estimates on third-party pages as unconfirmed unless the employer provides the figure.
Is the role full-time or an internship?
The detailed description identifies it as an internship, while the supplied summary says “Full-Time.” Confirm the arrangement with the employer or the current listing before accepting or applying.