AI Engineer Intern

Elimai.ai is an AI-native software company building next-generation intelligent platforms for

manufacturing, healthcare, and enterprise operations. We specialize in building intelligent

agentic workflows. We combine cutting-edge LLMs, specialized language models (SLMs), and

custom agents to solve real operational problems for mid-market businesses in India.

Our tech stack spans RAG systems, MCP servers, full-stack ML, and production-grade

inference pipelines. We ship, iterate, and learn in public. No slow bureaucracy.

The Role

As an AI Engineer Intern, you'll contribute to building production AI systems that process

millions of extracted structured/ unstructured data from unstructured documents and

orchestrate multi-step agentic workflows. This isn't a "learn Python" internshipit's hands-on

engineering on real products with real users.

What You'll Build

Agentic workflows: Design and implement multi-turn agent loops that parse emails, extract intent, fetch context from APIs, and generate structured responses using varied LLMs

MCP servers: Build Model Context Protocol integrations to connect AI agents with internal databases, ERPs (NetSuite, SAP), and custom tools. These become moats for our product.

LLM/SLM fine-tuning: Fine-tune small language models for domain-specific tasks (invoice extraction, PO parsing, buyer intent classification) on labeled datasets. Measure BLEU/F1 gains vs. base models.

RAG intelligence layers: Build retrieval pipelines over RabbitMQ emails and documentstores. Implement vector embeddings (text-embedding-3-small), vector DBs (Pinecone), and semantic search.

Full-stack ML: Train, evaluate, and deploy models end-to-end. Own the notebook experiment tracking (Weights & Biases) dataset versioning (DVC) inference server (FastAPI) monitoring pipeline.

Required Skills

Python expertise.LLM fundamentals: Understand prompt engineering, token budgeting,

context windows, and why you use different models for different tasks.

ML basics: Training/validation/test splits, evaluation metrics (F1, precision, recall),

overfitting, cross-validation. Can read a paper and implement it.

SQL & databases: Write efficient queries, understand indexing, normalization.

Comfortable with PostgreSQL or similar.

Git & DevOps basics: Version control, Docker basics, CI/CD pipelines. Understand why

we containerize inference.

Curiosity > credentials: Strong fundamentals beat fancy resume items. We care that

you can debug, think in systems, and ship.

Good-to-Have

Experience with LangChain, LlamaIndex, or similar agent frameworks.

Fine-tuning experience: LoRA, QLoRA, or custom training runs on HuggingFace models.

RAG at scale: Built or debugged RAG pipelines in production.

MCP experience: Built or integrated Model Context Protocol servers. Experience

working in VLMs is a plus.

What We Offer

10,000-14,000/month stipend: Hybrid internship in Coimbatore. Open for Initial Remote work but should move to Coimbatore if things are permanent.

Real code, real impact: Your work goes to production. You'll see users, revenue, and feedback.

Full-stack ownership: From idea training deployment monitoring. Own your responsibilities in the project end-to-end.

Direct mentorship: No corporate gatekeeping.

Potential extension: Strong performers can transition to a full-time role or continued contract work post-internship.

Day-to-Day Responsibilities

Build and ship features: Implement a component (agent, MCP server, fine-tuned model)

from spec to production in 1-2 week sprints.

Debug production issues: Investigate why an email pipeline broke, a model's accuracy

dropped, or an API returned garbage.

Read & understand papers: Stay current. We share research on in-context learning,

retrieval optimization, and agent architectures.

Participate in code reviews: Get feedback, give feedback. Shipping quality code is non-

negotiable.

Experiment & measure: Run A/B tests on prompt templates, model choices, and

retrieval strategies. Document findings.