ML/AI Intern

About ATRISI

ATRISI builds institutional intelligence infrastructure for the AI-native era.

About the Role

This internship is on the technical build track, focusing on hands-on ML and applied AI work to support platforms, programs, and internal research. It includes mentorship and clear deliverables.

Role Summary

We are hiring an ML / AI Intern to work on models, data, evaluation, and product-facing AI features within the ATRISI ecosystem. You will run scoped experiments, document results, and help prototype capabilities such as RAG, retrieval, and workflow intelligence, with code review and guidance from senior builders. This is a builder internship on real codebases.

Key Responsibilities

• ML & Model Work:
• Support training, fine-tuning, or evaluation on well-scoped tasks.
• Benchmark models and summarize tradeoffs (quality, latency, cost).
• Assist with dataset preparation, labeling guidelines, and data quality checks.
• Contribute reproducible notebooks or scripts (versioned, reviewable).
• AI Product Features:
• Prototype RAG, retrieval, or agent-style workflows aligned with platform and program needs.
• Run structured evaluations: accuracy, failure modes, edge cases, regression checks.
• Assist integrating model outputs into APIs or internal tools under senior review.
• Participate in code review and testing for AI-related changes.
• Research & Documentation:
• Write short experiment summaries for engineering and program teams.
• Capture learnings in internal notes or knowledge products.
• Support applied research pilots when they need ML instrumentation.

How We Work

• Async updates via GitHub, Notion, or agreed tools.
• Join working sessions when useful.
• Raise blockers, data issues, and scope risks early.

Requirements & Tech Stack

• Must Have:
• Pursuing or recently completed a degree in CS, AI, data science, or a closely related field.
• Strong Python and comfort with at least one of: PyTorch, scikit-learn, Hugging Face.
• Solid fundamentals: train/validation split, common metrics, overfitting, training vs inference.
• Ability to read documentation or papers and ship a small working experiment.
• Git discipline: clear commits, reproducible runs, honest reporting of results.
• Genuine interest in LLMs, RAG, or applied AI in products.
• Strong Signals (Prioritized):
• A GitHub or portfolio with ML projects (coursework, capstone, Kaggle, side projects).
• A short write-up of one project: problem → approach → stack → what you learned or shipped.
• Evidence of focus on evaluation, not only demos.
• Comfort picking tools (Python, Jupyter, HF, SQL, Docker) and learning the rest on the job.
• Nice to Have:
• Vector DBs, embeddings, or orchestration (e.g. LangChain-style) experience.
• FastAPI, Node, or light full-stack glue for wiring models to products.
• Builder-style work: deployed demo, RAG app, agent workflow, or Amplify-like capstone.
• Notebook, blog post, or open-source contribution.

What Not To Expect

• Generic “AI awareness” or prompt-playground work with no code.
• Exclusively live teaching or cohort operations.
• Inability to commit to regular async progress and documented deliverables.

What You Will Gain

• Mentorship on production-minded ML and AI feature work.
• Exposure to a multi-platform AI-native ecosystem (platforms + programs + research).
• Portfolio artifacts tied to real institutional and student-facing initiatives.
• A credible path toward ATRISI Fellowship product engineering, or research associate tracks for strong performers.

Logistics

• Employment type: Internship (weekly availability discussed at apply)
• Location: Bengaluru, India