ML/AI Intern

Job Description

ML / AI Intern

Team: Technical Build & Product

#About ATRISI

ATRISI builds institutional intelligence infrastructure for the AI-native era — through applied research, enablement programs (Amplify with AI, Resonance with AI), and platforms including JoaLLM and TWAI. We ship real systems, experiments, and deployable workflows — not slide-only pilots.

This internship sits on the technical build track: hands-on ML and applied AI work that supports platforms, programs, and internal research — with mentorship and clear deliverables.

Role summary

We are hiring an ML / AI Intern to work on models, data, evaluation, and product-facing AI features inside 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. It is not a passive research observer role or a student-facing program coordination role (see our Learning Experience Associate track for that).

What you will do

• Support training, fine-tuning, or evaluation on well-scoped tasks
• Benchmark models and summarize tradeoffs (quality, latency, cost)
• Help with dataset prep, labeling guidelines, and data quality checks
• Contribute reproducible notebooks or scripts (versioned, reviewable)

• Prototype RAG, retrieval, or agent-style workflows aligned with platform and program needs
• Run structured evals: 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

• 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

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

What we are looking for

• 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

• 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 you care about evaluation, not only demos (it runs once)
• Comfort picking tools (Python, Jupyter, HF, SQL, Docker) and learning the rest on the job

• 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

• You only want generic AI awareness or prompt-playground work with no code
• You expect exclusively live teaching or cohort operations (that is a different role)
• You cannot commit to regular async progress and documented deliverables

• 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

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

• ATRISI site (recommended): https://atrisi.org/collaborate/ml-ai-intern#apply
• Email: [Confidential Information] — Subject: Application: ML / AI Intern — resume + GitHub/portfolio link + 1-paragraph project summary
• Builder Challenge:https://atrisi.org/programs/amplify-with-ai/builder-challenge

In the application, be ready to describe *an ML or AI project you built (or want to build here) in a few sentences — problem, approach, tools, and outcome.