This is a model-building role.
You will train, fine-tune, evaluate, and optimize deep learning models that run in production against live traffic. Your day is spent on loss functions, data distributions, decision thresholds, calibration, and inference numerics. It is not spent wiring together third-party APIs or assembling agent frameworks.
If your recent work is mostly prompt engineering, RAG plumbing, and orchestrating hosted model APIs, this is not the right role, and that is fine. We are hiring people who open the model and change what is inside it, and who can explain, from first principles, why the change worked. If you have trained a model from scratch, debugged why it would not converge, and then made it 10x cheaper to serve without losing accuracy, keep reading.
The scale you will operate at
We run 40+ production ML models across two large families, on a mixed CPU and GPU fleet.
Documents: Region of Interest detection, Photocopy Classifier, Text Tampering, Photo Tampering, Readability, OCR, Named Entity Recognition, PII Masking.
Faces: Face Detection, Face Quality,
Sunglass Detection, NSFW, Face Mask Detection, Face Match, Liveness Detection, Deepfake Detection. The production footprint: - - - - - 2000 requests per second at peak 25 TB of serving RAM ~18,000 CPUs 120 NVIDIA L4 GPUs 2M+ verifications per day At this scale, cost-to-serve is a first-class engineering constraint. A model that is 2 percentage points more accurate but 10x more expensive to run may be the wrong model. You will own that tradeoff with numbers, not opinions.
What you will do
Frame ambiguous problems mathematically. Turn a business requirement like "catch deepfakes in KYC" into a well-posed objective: the right positive class, the right operating point, the right validation protocol that does not leak, and a metric that survives class-prevalence shift.
Train models, end to end. Own the full lifecycle for one or more model families: problem framing, data strategy, architecture
📌 Senior ML Engineer (Mumbai)
🏢 IDfy
📍 Mumbai