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 choice, training, evaluation, deployment, monitoring, and retraining. You are accountable for the model in production, not just the notebook.
Evaluate with rigor. Design evaluation that predicts production behavior. Confusion matrices, ROC and PR curves, TPR at a fixed low FPR, calibration error, and cross-distribution generalization. Know why AUC can lie about a model you operate at FPR = 1e-4.
Optimize for inference at scale. Quantize, prune, and re-architect models so they serve within latency, throughput, and cost budgets. Move workloads off GPU to quantized CPU inference where the numbers justify it, and prove the accuracy held with a production canary before, not after. Deploy and operate resilient production systems that run 24x7
Reproduce and improve on research. Read a
📌 Senior ML Engineer (Mumbai)
🏢 IDfy
📍 Mumbai