Location
GIFT City, Gandhinagar, Gujarat
Work Mode
Hybrid
Duration
3 months
Stipend
₹30,000/mo
Start Date
Immediate / Rolling
Openings
11 positions
Type
Full-time
Category
Machine Learning
About the Internship
BNP Paribas India is hiring a MLOps Intern in GIFT City, Gandhinagar — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to MLOps rather than a certificate-only programme.
About the role
As a MLOps Intern at BNP Paribas India, your core focus will be…
About BNP Paribas India
BNP Paribas India offers a mentor-led MLOps Intern in GIFT City, Gandhinagar with live project work in machine learning, a stipend of ₹30,000/month and a Pre-Placement Offer opportunity for strong performers.
See all BNP Paribas Indiainternships →GIFT City, Gandhinagar, GujaratVerified employer11 openings
Apply to BNP Paribas India →Skills Required
MLflowDockerCI/CD for MLModel RegistryKubernetes BasicsPythonData Versioning (DVC)Model MonitoringCloud ML ServicesGit
Who Can Apply
- Final-year students and recent graduates from B.E./B.Tech (CSE/IT/ECE), BCA/MCA or B.Sc/M.Sc (Computer Science / IT) who are based in or willing to work from GIFT City, Gandhinagar. You should be keen to learn MLflow, Docker, CI/CD for ML on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.
Perks
Internship CertificateLetter of RecommendationMentorship from a named mentorLive project experienceExposure to professional tools and workflowsStructured onboarding and a formal mentor at a large, established employer
Interview & Selection Process
4 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Practical assignment or case study with a short walkthrough
- 3Discussion with the hiring manager on the role and your fit
- 4HR discussion — stipend, start date, expectations and offer
₹30,000/month
Stipend · 3 months
Openings11 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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