State Street

AI/ML Engineering Intern

InternshipIn OfficeRanchi, Jharkhand3 months23K/mo3 openingsImmediate / Rolling

About this internship

Join State Street in Ranchi as a AI/ML Engineering Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on AI engineering work. About the role As a AI/ML Engineering Intern at State Street, your core focus…

Skills you'll use

PythonFastAPIREST APIsLLM IntegrationDockerModel DeploymentCloud PlatformsGitPrompt EngineeringTesting

What you'll do

Work on wiring models into user-facing features, APIs and pipelines alongside the core team
Apply Python, FastAPI, REST APIs, LLM Integration to live, production-facing problems
Document your work clearly for reviewers and stakeholders
Participate in reviews and act on the feedback you receive
Collaborate across engineering, product, design or business as required
Follow the team's quality, testing and documentation standards

Who can apply

Pursuing or recently completed B.E./B.Tech/BCA/MCA/M.Sc (CS, AI) — 2026 freshers & final-year students welcome
Working knowledge of Python, FastAPI, REST APIs (or a genuine willingness to learn them)
Strong fundamentals in your discipline and clear problem-solving ability
Care with detail and the discipline to document your work
Clear written and verbal communication in English
Based in or willing to work from Ranchi, Jharkhand

Perks & benefits

Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hours

Interview & selection process

2 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Online aptitude / domain assessment

About State Street

State Street operates in the financial services sector and has an active presence in Ranchi. Interns here are put on real work from the start, because the team's view is that the fastest way to learn a craft is to practise it under people who already do it well — not to watch it from a…

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