AI Platform
/Senior leadership
Staff LLM Engineer — Foundation Models
LLM Training & Serving
Compensation
INR 50 - 80 LPA + equity.
Engagement
Full-Time
Permanent role. Full-time commitment. Primarily on-site, with limited remote flexibility.
Scope of role
Set direction within your domain. Build and mentor a team. Own outcomes at the function level.
01 — The role
Why this role exists at EduRankAI
Own the LLM training stack. Pre-train, supervised fine-tune, RLHF/DPO, evaluation, release. You will lead the recipes that produce EduRankAI's foundation models and you will be the engineer the rest of the platform calls when training stalls at 3am. This is a hands-on Staff role. You will write code, run trainings, debug NaNs, profile kernels. You will also mentor a small team of mid/senior LLM engineers and shape our training methodology over multiple model generations.
02 — The work
What you will own
- 01 Own the pre-training pipeline for 1B → 70B parameter transformer models.
- 02 Lead SFT and preference-optimisation runs (RLHF, DPO, KTO, IPO as the field moves).
- 03 Design the eval harness — Indic reasoning, factuality, safety, refusal calibration.
- 04 Drive inference optimisation jointly with the serving team (KV cache, quantisation, batching).
- 05 Mentor the LLM engineers; bring at least one to Staff readiness over 24 months.
- 06 Author public technical reports where the work merits it.
03 — The expertise
What we look for
04 — The bar
Who thrives here
- → You have led at least one foundation-model training run at 7B+ parameters on a cluster you owned or operated.
- → You can debug a loss curve and propose a hypothesis within an hour.
- → You have published or open-sourced ML/LLM work the field has cited or used.
- → You can read the latest frontier-paper repo, identify why the training method is fragile, and write a one-pager about how to harden it.
- → You write your own evals.
05 — Hiring process
What to expect after you apply
- 01
Application review
Every application is read personally within five business days. We respond either way.
- 02
Take-home or live exercise
Role-specific. Time-boxed. Real problems we are actually working on, not invented puzzles.
- 03
Conversations
Deep technical and values conversations with the team you would join. No trick questions. No panel ambushes.
- 04
Offer or honest no
If yes: digital offer letter, signed in-portal, transparent terms. If no: written feedback if you want it.
Before you start
What we will collect. What it costs. What we will not do with it.
We will collect
- Name, email, phone — Account + application updates. No marketing.
- Resume / portfolio link — Human review of your work.
- Date + place of birth — Identity verification only.
- Your written responses — Selection rubric. Read by humans.
- Government ID (later) — Anti-fraud at offer / interview stage. Not at signup.
We will never
- Sell your data
- Share with third-party recruiters
- Use for advertising
- Train models on it
- Send marketing email
Our situation
EduRankAI is a small, independent organization building long-term capabilities in educational intelligence, advanced AI systems, and research infrastructure. We take no advertiser money, no donations with strings attached, and no investor pressure on hiring decisions. Applying is free, and every application is read by a human — recruitment, technical, academic and leadership teams. It buys us the right to be honest.
Ready to apply?
We read every application personally. If you are the right person for this role — regardless of pedigree, background, or where you are based — you will hear back from us within five business days.
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