Intern - Machine Learning Engineer

Vajro; Chennai District; On-site; INR 167,000 - 335,000; Full time

Benefits offered by this job

Mentorship

Hands-on experience

Production exposure

Job summary

Vajro is seeking a hands-on Machine Learning Engineering Intern with a strong understanding of the modern AI stack. You will be embedded directly in our engineering team and contribute to AI pipelines, APIs, and agent systems under the mentorship of senior ML engineers.

Expect meaningful responsibility, thoughtful code reviews, and production exposure from day one. In this role you will work on RAG systems, AI agents, and Flask- or FastAPI-based APIs that wrap AI capabilities for client-facing

Qualifications

• Understanding of how LLMs work, including tokenization, context windows and guardrails.
• Ability to run inference on local LLMs using tools like Ollama, VLLM, or Hugging Face Transformers.
• Familiarity with RAG architecture, embeddings, retrieval, and generation.
• Strong Python skills with clean, idiomatic code and type hints.
• Experience with JSON schema, Pydantic models, and data validation patterns.

Responsibilities

• Contribute to AI pipelines, APIs, and agent systems under mentorship.
• Embed directly in the engineering team with production exposure from day one.
• Participate in code reviews and design discussions for scalable AI solutions.
• Develop and test systems for multi-source retrieval and structured outputs.

Skills

LLM Fundamentals

Prompting techniques

API design

Python programming

Code navigation with AI

Git & CI

JSON schema & validation

Distributed systems basics

Education

Pursuing Bachelors in CS/AI

Tools

Ollama

VLLM

Hugging Face Transformers

LangChain

LlamaIndex

Job description

We are looking for a hands-on Machine Learning Engineering Intern with a strong understanding of the modern AI stack. The ideal candidate understands how LLMs work, can navigate a real codebase, and is comfortable using AI tools to accelerate their work without becoming dependent on them.

You will be embedded directly in our engineering team and contribute to AI pipelines, APIs, and agent systems under the mentorship of senior ML engineers. Expect meaningful responsibility, thoughtful code reviews, and production exposure from day one.

Must-Have Requirements

The following are non-negotiable. Candidates who cannot demonstrate these will not be considered regardless of other strengths.

LLM Fundamentals & Prompting

Clear understanding of how LLMs work, including tokenization, context windows, temperature, structured output formatting, LLM guardrails, and hallucination prevention.

• Proficiency in prompting techniques such as few-shot prompting, system prompts, structured outputs, role prompting, and reasoning-oriented prompts.
• Familiarity with recent model releases, capability shifts, and architectural developments.
• Ability to run inference on local LLMs using tools like Ollama, VLLM, or Hugging Face Transformers
• Moderate to strong understanding of RAG architecture, including chunking, embeddings, retrieval, reranking, and generation.
• Working knowledge of AI agents, tool use, and agent orchestration frameworks such as LangChain, LlamaIndex, AutoGen, or custom frameworks.
• Understanding of MCP (Model Context Protocol) and AI skills/tool design
• Transformer architecture basics — attention mechanism, encoder/decoder, positional encoding, embeddings
• Strong Python skills, including clean, idiomatic code, proper error handling, algorithm design, and type hints.
• Ability to work with structured outputs: JSON schema, Pydantic models, data validation patterns
• Fundamental understanding of API scaling, including async and sync patterns and basic load considerations.
• Version control proficiency: Git branching, PRs, commit hygiene, resolving conflicts

AI Tools & Codebase Navigation

• Ability to independently navigate an existing, non-trivial codebase using AI-assisted tools (Cursor, Claude, ChatGPT, GitHub Copilot, etc.)
• Uses AI tools to boost velocity — but can reason through code independently and does not require AI to explain every line

Computer Vision & Document AI (baseline knowledge required; expertise is not mandatory)

• Rough working knowledge of image processing concepts: preprocessing, transformations, color spaces
• Familiarity with OCR tools and their practical limitations (Tesseract, Docling, AWS Textract, etc.).
• Basic awareness of object detection concepts (bounding boxes, YOLO-style models)
• Ability to articulate technical decisions clearly in review calls and project syncs without needing repeated prompting to explain reasoning.
• Comfortable discussing system design trade-offs and architecture choices with leads and peers
• Strong written communication for async updates, PRs, and documentation

Good-to-Have Skills

Candidates with these will have an advantage, but they are not disqualifying to be missing.

• Docker: containerizing Python services, multi-stage builds, docker-compose for local stacks
• Deep RAG expertise: Graph RAG, hybrid retrieval, vector database internals (Pinecone, Weaviate, Qdrant, pgvector)
• Agent observability: tracing, logging agent runs, dashboards (LangSmith, Phoenix, custom)
• Frontend / UI basics: HTML/CSS/JS or Streamlit for internal tooling and demos
• Active participation in product and solution architecture discussions
• Awareness of the latest releases, frameworks, and modern technologies, with the ability to choose current, practical solutions instead of relying on outdated approaches.

What We Expect From You

• You will be assigned tasks and expected to drive them to completion with minimal hand-holding.
• You should be able to take a vague requirement, ask the right clarifying questions, and convert it into working code.

AI-augmented, not AI-dependent

• We expect you to use AI tools to move faster; that is the right instinct.
• We do not expect you to rely on AI for tasks you should already understand, such as debugging your own logic, reading stack traces, or justifying architectural decisions.

Takes direction well, executes better

• You should be able to absorb guidance from managers and tech leads and translate it into concrete action without repeated follow-up
• Keep feedback loops tight by flagging blockers early and communicating progress proactively.

High standards

• Our systems are client-facing, so code quality, correctness, and documentation matter.
• You will be expected to review your own work before pushing it, not just submit first drafts

What You Will Work On

• RAG systems with multi-source retrieval, reranking, and structured output formatting
• AI agent workflows with tool use, memory, and multi-step orchestration
• Flask- or FastAPI-based APIs that wrap AI capabilities for client-facing deployment
• Local LLM inference setups and evaluation harnesses
• Prompt engineering, evaluation, and iterative system improvement
• Client-facing projects with real-world business impact
• No-code and low-code solution development for internal tools and product prototypes

This internship is designed for candidates who want to build practical AI systems, learn from experienced engineers, and contribute to products that solve real client problems.

We build real AI systems. If that excites you, we want to hear from you.