ML / AI Intern Organization: ATRISI (Applied Technology & Research Institute for Social Impact)

Location:
Bengaluru, India Type: Internship 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 ML & model work
- 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) AI product features
- 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 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 What we are looking for Must have
- Pursuing or recently completed a degree in CS, AI, data science, or a closely related field
- Robust 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 (what we prioritize in review)
- 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 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 Not the right fit if
- 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 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)
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Location:
Bengaluru, India

How to apply
- ATRISI site (recommended): https://atrisi.org/collaborate/ml-ai-intern#apply
- Email: [email protected] — 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.

📌 ML/AI Intern (Bengaluru)
🏢 ATRISI
📍 Bengaluru