AI and machine learning jobs and internships in India
CareerID lists AI, machine learning and data science jobs and internships from companies whose verification CareerID has approved. A posting can attach a proctored JD Score test generated from its job description, so candidates without a master's degree or a well-known college are ranked on the Python, ML and data work the role asks for.
Listings update as employers publish. Page reviewed 28 Sept 2026.
Open AI and ML roles
The roles below are pulled live from published postings whose title or required skills match AI, machine learning, NLP, computer vision or data science work, newest first. Each links to the full description, stated pay and deadline.
No AI and ML roles are open right now.
New postings appear here as soon as employers publish them. Meanwhile you can browse every open role or create a profile so you are ready to apply.
Skills often listed for AI and ML roles
A general guide. Each posting states its own requirements.
- Python
- PyTorch
- TensorFlow
- NLP
- LLMs
- SQL
How applicants are assessed
A posting can attach a JD Score test generated from its own job description. It is proctored, runs in four layers and produces one score out of 10. How the JD Score works.
L0 · Speed & Coverage20%
Round-robin rapid MCQs — faster correct answers score higher.
L1 · Understanding20%
Per-skill blocks: micro → trap → scenario questions that catch memorisers.
L2 · Execution30%
Do the actual work in a live task or practical block.
L3 · Real-World Validation30%
Big real-world scenarios: decide, then defend your approach.
Frequently asked questions
Can I get an AI or ML job on CareerID without a master's degree?
CareerID does not filter on degree or college, and postings with a JD Score test rank applicants on the score. Individual employers can still state education requirements in their own job description, so check the posting.
What does an AI/ML skill test include?
It is generated from the job description. Layer L0 covers breadth across the listed skills, L1 per-skill understanding including trap questions, L2 hands-on work such as Python tasks run against test cases where the skill supports it, and L3 real-world scenarios. The four layers are weighted 20%, 20%, 30% and 30% into a single score out of 10.