Machine Learning Intern

Enterpret, Bengaluru +1

What you will do

Build and prototype LLM-backed features alongside senior engineers — retrieval pipelines, agents, evaluation harnesses, or hybrid patterns — and ship a meaningful slice of one into production by the end of the internship.
Contribute to eval rubrics, test plans, and quality dashboards that make AI feature quality measurable, and run evals on shipped systems.
Write clean, testable Python in our production stack, add tests that catch regressions, and help monitor and harden systems already in production.
Debug failures across data, infra, model, and prompt logic with your mentor, and write up what you learn so the team learns with you.
Collaborate with engineers, product, and the AI team — joining design reviews, shipping PRs, and participating in design decisions on features you contribute to.
Continuously raise the bar on your own craft: ask sharp questions in code reviews, read the systems before you change them, and push for quality without being asked.

What It Takes?

Demonstrated AI/ML project depth – coursework, research, open-source, hackathons, or prior internships where you've built something non-trivial with LLMs and evaluated it rigorously.

Enterpret, Bengaluru +1

Build and prototype LLM-backed features alongside senior engineers — retrieval pipelines, agents, evaluation harnesses, or hybrid patterns — and ship a meaningful slice of one into production by the end of the internship.
Contribute to eval rubrics, test plans, and quality dashboards that make AI feature quality measurable, and run evals on shipped systems.
Write clean, testable Python in our production stack, add tests that catch regressions, and help monitor and harden systems already in production.
Debug failures across data, infra, model, and prompt logic with your mentor, and write up what you learn so the team learns with you.
Collaborate with engineers, product, and the AI team — joining design reviews, shipping PRs, and participating in design decisions on features you contribute to.
Continuously raise the bar on your own craft: ask sharp questions in code reviews, read the systems before you change them, and push for quality without being asked.

What It Takes?

Demonstrated AI/ML project depth – coursework, research, open-source, hackathons, or prior internships where you've built something non-trivial with LLMs and evaluated it rigorously.