Muvin

Machine Learning Engineer Intern

InternshipHybridNanded, Maharashtra6 months24K/mo4 openingsImmediate / Rolling

About this internship

Join Muvin in Nanded as a Machine Learning Engineer Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on applied machine learning and model deployment work. About the role As a Machine Learning Engineer…

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Skills you'll use

PythonTensorFlowScikit-LearnModel Deployment (MLOps)Feature EngineeringPandas & NumPyModel Evaluation MetricsDockerREST / gRPC Model ServingExperiment Tracking (MLflow)

What you'll do

Work on training, packaging and deploying machine-learning models into production alongside the core team
Apply Python, TensorFlow, Scikit-Learn, Model Deployment (MLOps) 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 (CSE/IT/ECE), BCA, MCA, M.Sc (CS / Statistics) or M.Tech — 2026 freshers & final-year students welcome
Working knowledge of Python, TensorFlow, Scikit-Learn (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 Nanded, Maharashtra

Perks & benefits

Flexible working hoursNetworking with senior professionalsInternship CertificateLetter of RecommendationMentorship from a named mentor

Interview & selection process

2 rounds — typical selection flow for this role:

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

About Muvin

Muvin operates in the teen neobanking sector and has an active presence in Nanded. 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 distance. The work…

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