Morgan Stanley

AI/ML Engineering Intern

InternshipWork from HomeHosur, Tamil Nadu3 months34K/mo3 openingsImmediate / Rolling

About this internship

Start your career in AI engineering with Morgan Stanley as a AI/ML Engineering Intern in Hosur. You will sit with a working team, use the same tools and systems they do, and own a defined piece of work from day one. About the role As a AI/ML Engineering Intern at Morgan Stanley, 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 Hosur, Tamil Nadu

Perks & benefits

Pre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship CertificateLetter of Recommendation

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Technical interview on fundamentals and your projects
3HR discussion — stipend, start date, expectations and offer

About Morgan Stanley

Morgan Stanley operates in the investment banking sector and has an active presence in Hosur. 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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