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CynLr logo — CynLr Recommendation Systems Intern in Jaipur, Rajasthan
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Recommendation Systems Intern

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Location

Jaipur, Rajasthan

Work Mode

In Office

Duration

2 months

Stipend

₹19,000/mo

Start Date

Immediate / Rolling

Openings

14 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹19,000/moApply on Company Website ↗

About the Internship

Start your career in recommendation and personalisation systems with CynLr as a Recommendation Systems Intern in Jaipur. 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 Recommendation Systems Intern at…

About CynLr

CynLr offers a mentor-led Recommendation Systems Intern in Jaipur 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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Jaipur, RajasthanVerified employer14 openings
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Skills Required

Recommendation SystemsCollaborative FilteringPythonRanking ModelsEmbeddingsA/B TestingSQLCold-Start HandlingEvaluation (NDCG / CTR)Feature Engineering

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT), MCA, M.Sc (CS / Statistics) or M.Tech who are based in or willing to work from Jaipur. You should be keen to learn Recommendation Systems, Collaborative Filtering, Python 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

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

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Technical interview on fundamentals and your projects
  3. 3Practical assignment or case study with a short walkthrough
  4. 4HR discussion — stipend, start date, expectations and offer
19,000/month

Stipend · 2 months

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Openings14 positions
Applicants0 applied
ModeIn Office
CategoryMachine Learning
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