Razorpay is Hiring AI Builder Interns | Bangalore | Students (No Resume Screening)

Job Description

Razorpay's AI Builder Internship is worth flagging separately from a typical internship listing because of how differently it selects candidates — the program's own framing, "Build. Show. Get hired," is a literal description of the process: no resume screen, no aptitude test, no group discussion. If you're a student who's been building AI projects on the side but doesn't have a brand-name college or a high CGPA to lean on, this is one of the more genuinely merit-based entry points into a large Indian fintech company currently available.

The stipend is the detail worth leading with: 𝐑𝐬. 𝟕𝟓,𝟎𝟎𝟎 𝐩𝐞𝐫 𝐦𝐨𝐧𝐭𝐡 is well above what most students see through standard campus placement internships, which tells you something about the selection bar even though there's no formal screening stage — a high-paying, screening-light program like this will still draw a highly competitive applicant pool, just filtered on a different axis (demonstrated build quality instead of academic credentials).

What this means practically for a fresher-oriented reader: the absence of a resume/CGPA filter is genuinely rare among large-company hiring programs in India, and it specifically benefits students at colleges that don't typically get shortlisted through conventional campus drives. But "no resume screen" doesn't mean "no bar" — it likely means the bar has simply moved entirely onto your ability to show a working project and defend your technical decisions, which for many students is a harder, less familiar test than a standard interview.

𝐎𝐮𝐫 𝐓𝐚𝐤𝐞:

- 𝐆𝐨𝐨𝐝 𝐟𝐨𝐫: students with existing AI/ML or technical projects (even unpolished ones) who want a genuinely resume-blind evaluation process

- 𝐍𝐨𝐭 𝐢𝐝𝐞𝐚𝐥 𝐟𝐨𝐫: students without any project experience hoping to prepare from scratch before the deadline — treat this listing as motivation to start building rather than something to cram for last-minute

- 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲: high despite the low-friction screening — a stipend this far above market average for a student role will attract strong competition based purely on build quality

- 𝐖𝐡𝐲 𝐢𝐭'𝐬 𝐰𝐨𝐫𝐭𝐡 𝐚𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐞𝐯𝐞𝐧 𝐢𝐟 𝐮𝐧𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥: since evaluation is build-based rather than resume-based, you walk away with a demonstrable project regardless of outcome — useful for other applications

𝐂𝐨𝐦𝐩𝐞𝐧𝐬𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: The stipend is explicitly stated at ₹75,000/month — notably transparent compared to most listings on this site, which typically don't disclose pay. For context, this is meaningfully above typical student internship stipends in India across most sectors, reflecting both Razorpay's scale as a major fintech and the specialized AI skill set the program is targeting.

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Since detailed eligibility (year of study, branch requirements) isn't fully specified beyond "currently enrolled students," confirm the fine print directly on Razorpay's official application page rather than assuming you either do or don't qualify based on this summary alone — it's a quick check that avoids ruling yourself out prematurely or wasting effort if you're not eligible.

Roles & Responsibilities

The responsibilities emphasize output quality and independence over collaborative feature-building — this role centers on consistently delivering accurate, timely data rather than building new product functionality.

Qualifications & Eligibility

Eligibility is intentionally broad on paper but the real qualifying bar is a demonstrable project — worth reading this section as a floor, not a full picture of what's competitive.

- Currently enrolled student

- No CGPA or resume-based cutoff

- In-person in Bangalore for 6–12 months

Skills Required

The listing itself doesn't specify a skills list directly — the program instead evaluates skill indirectly through your submitted project and your ability to explain it, making a working build the real prerequisite rather than any stated technology list.

- Demonstrated ability to build and ship a working AI-related project

- Comfort explaining technical/architectural decisions clearly under panel questioning