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Emkay Global Financial Services logo — Emkay Global Financial Services Machine Learning Engineer Intern in Hubli, Karnataka
Emkay Global Financial Services VerifiedActively Hiring

Machine Learning Engineer Intern

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Location

Hubli, Karnataka

Work Mode

Hybrid

Duration

2 months

Stipend

₹40,000/mo

Start Date

Immediate / Rolling

Openings

4 positions

Type

Full-time

Category

Machine Learning

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About the Internship

Join Emkay Global Financial Services in Hubli 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…

About Emkay Global Financial Services

Emkay Global Financial Services offers a mentor-led Machine Learning Engineer Intern in Hubli with live project work in machine learning, a stipend of ₹40,000/month and a Pre-Placement Offer opportunity for strong performers.
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Hubli, KarnatakaVerified employer4 openings
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Skills Required

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

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT/ECE), BCA, MCA, M.Sc (CS / Statistics) or M.Tech who are based in or willing to work from Hubli. You should be keen to learn Python, TensorFlow, Scikit-Learn 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

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:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Technical interview on fundamentals and your projects
40,000/month

Stipend · 2 months

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Openings4 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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