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Mintoak logo — Mintoak Machine Learning Engineer Intern in Silchar, Assam
Mintoak VerifiedActively Hiring

Machine Learning Engineer Intern

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Location

Silchar, Assam

Work Mode

Hybrid

Duration

6 months

Stipend

₹19,000/mo

Start Date

Immediate / Rolling

Openings

4 positions

Type

Full-time

Category

Machine Learning

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About the Internship

Mintoak is hiring a Machine Learning Engineer Intern in Silchar — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to applied machine learning and model deployment rather than a certificate-only programme. About the role As a Machine Learning Engineer…

About Mintoak

Mintoak offers a mentor-led Machine Learning Engineer Intern in Silchar with live project work in machine learning, a stipend of ₹19,000/month and a Pre-Placement Offer opportunity for strong performers.
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Silchar, AssamVerified 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 Silchar. 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

Exposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship Certificate

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Online aptitude / domain assessment
  3. 3Technical interview on fundamentals and your projects
  4. 4HR discussion — stipend, start date, expectations and offer
19,000/month

Stipend · 6 months

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Openings4 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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