We are a AI-first career and hiring platform building the behavioral intelligence infrastructure for the future of hiring. Our mission is to create an intelligent system that understands candidates beyond resumes and helps organizations make better hiring decisions. Through AI-powered career coaches, we help candidates prepare for every stage of their interview journey.

On the other side, our AI-native recruitment platform enables companies to discover, assess, and hire talent more intelligently and efficiently.

We're a small, fast-moving team that believes speed, ownership, and AI-native execution create extraordinary products. You'll work directly with the founders and the CTO, with real influence over both the product and the company from an early stage.

The Role

You will build the first version of Hiring Intelligence models — the system that turns recorded interview transcripts into evidence-backed candidate profiles and explainable, ranked shortlists that real recruiters act on.

This is an applied ML role, not a research role.

The center of gravity is: LLM-based structured extraction done with engineering discipline, a transparent matching/scoring model, and the evaluation harness that proves (or disproves) that any of it works. You will own this pipeline end to end — from raw transcript to number-indatabase — and ship it to production.

We operate on one hard rule: LLMs extract evidence; they never make the final prediction. Every score the system produces must decompose to evidence a recruiter or auditor can inspect. If that constraint excites you rather than annoys you, you're our profile.

What you will do

- Build the extraction pipeline: LLM-based structured extraction of competency evidence from

interview transcripts — versioned prompts and rubrics, persisted outputs with provenance, evidence quotes, measured accuracy against expert labels. Treated as an engineering system, not prompt tinkering.
- Build the v1 scoring/matching model: a transparent,



requirement-weighted scoring model that ranks

candidates against role requirements — interpretability is a hard constraint. Lay the groundwork for learning-to-rank as recruiter-preference data accumulates.
- Build the evaluation harness: precision@K against recruiter picks, agreement with expert labels,

calibration where used, and subgroup fairness checks on ranked outcomes. Every model version ships with an evaluation report.
- Run label operations: design and run the expert-labelling loop with our SMEs and the recruiterpreference capture; own training-data snapshots and model versioning.
- Ship to production: own the full lifecycle — prototyping, evaluation, deployment, monitoring —

working with Python, Postgres, S3, and standard job-queue/batch infrastructure.
- Work independently: take ambiguous problems, figure out the right approach, propose it in writing,

and drive it to production with minimal hand-holding. Must-Have

Required Skills & Qualifications

- 4-6 years hands-on experience building and shipping ML/AI products to production — and keeping

them running. Not just research or notebooks.
- LLM-as-component discipline: you have built structured-extraction or LLM-pipeline systems with

versioned prompts and measured accuracy — you treat LLM output as a data source to be evaluated, not an oracle.
- Classical ML fundamentals: linear/gradient-boosted models, regularization, honest train/test hygiene;

you know what leaks and what overfits when you have only a few hundred labels.
- Evaluation literacy: you can design an experiment, argue precision@K vs AUC vs calibration, and





you are willing to say "this doesn’t work" when the numbers say so.
- Strong Python and the modern ML stack (PyTorch / scikit-learn / Hugging Face or equivalent), plus

comfort with data plumbing (SQL, batch pipelines, Docker).
- A builder's mindset with proven independent ownership — you've shipped real products end to end.

Bonus Points
- Learning-to-rank or recommender-system experience.
- Prior hiring-tech or assessment-tech exposure, or awareness of its regulatory landscape (NYC LL144, EU AI Act).
- Fairness/bias measurement experience on ranked or scored outcomes.
- Speech/ASR or multimodal (audio/video) feature-extraction experience — this is our roadmap, not the

day-one job.
- Vector search / embedding-retrieval experience.
- Early-stage startup or founder-led environment experience.

Explicitly Not This Role
- Not a research position — no papers, no novel architectures.
- Not a prompt-engineering-only role — pipelines, models, and evaluation are the job.
- Not a notebook-DS role — what you build, you deploy.

What Success Looks Like
- 30 days: extraction pipeline producing versioned, evidence-linked outputs on real interview transcripts;

evaluation scaffold running.
- 60 days: v1 requirement-weighted scoring model live behind a shadow run; expert-label loop in

motion; first accuracy and fairness report.
- 90 days: a written, statistically honest read on model quality against recruiter picks — including what

to keep, what to fix, and what to kill.

What We Provide

- Full access to up-to-date AI tooling — Claude, coding assistants, and infrastructure support.
- Direct access to the CTO and founders, with real influence over technical and product decisions.
- Ownership of an entire intelligence layer whose outputs shape real hiring decisions.

We are looking for someone who wants to help build an AI-native operating system for hiring and career intelligence. Skills: ml,python,sql,llm,ai,pytorch

📌 Applied AI/ML Engineer (Gurugram)
🏢 Magnet HR Tech Digital
📍 Gurugram