[Hiring For]: Senior ML Engineer

Role overview

[Hiring For]: Senior ML Engineer at IDfy

Why this role is rare Most "AI" roles today are integration roles: call a hosted model, shape a prompt, ship.

Responsibilities

• You will train, fine-tune, evaluate, and optimize deep learning models that run in production against live traffic.
• You will own that tradeoff with numbers, not opinions.
• Design evaluation that predicts production behavior.
• Lead.
• Review models, not just code.

Employment details

• Location: Mumbai [Maharashtra] [India]

About the employer

• IDfy

Additional role details

• This is a model-building role.
• 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
• 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.
• 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.
• 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 paper, reproduce its results, strip it down to what actually matters for our constraints, and ship it.
• We value simplification that preserves accuracy.
• A smaller, cheaper model that matches a heavier one is treated as a genuine result here, not a compromise.
• Mentor 3 to 6 junior engineers on math-driven problem solving, experiment design, and ML systems.
• Set the bar for source discipline and reproducibility.
• What you bring ML fundamentals, deeply held.
• Probability and statistics: distributions, estimation, hypothesis testing, class prevalence and its effect on precision, sampling.
• Optimization: gradient descent and its variants, loss landscapes, convergence behavior, why training diverges and how to fix it.
• Linear algebra: enough to reason about embeddings, projections, and what a layer is actually computing.
• You can derive, not just import.
• If asked why a loss is shaped the way it is, or what changes when you quantize a layer to INT8, you can work it out on a whiteboard.
• Proven model-training experience.
• You have trained deep learning models from scratch and fine-tuned pretrained networks, in Py Torch.
• Training loops, data pipelines, augmentation, and loss design are things you have written, not things you have called.
• Depth in at least one of Computer Vision (detection, classification, metric or embedding learning, anti-spoofing) or NLP (sequence labeling, NER, transformer models), with working literacy in the other.
• You have shipped at least two models into production, operated them at some scale and kept them healthy through drift and adversarial pressure.
• Evaluation and calibration.
• You design evaluations that do not overstate performance: leakage-free splits, cross-generator or cross-source generalization tests, and operating points chosen for the real cost asymmetry.
• You understand calibration and can reason about decision thresholds under changing prevalence.
• Production ML at scale.
• 4+ years building and operating large-scale ML systems: real-time inference, monitoring, drift detection, and retraining.
• Inference optimization: quantization (INT8, BF16), and hands-on with at least one of Open VINO, CTranslate2, Triton, or v LLM.
• You reason about latency, throughput, and cost-to-serve as engineering targets.
• Comfortable on Kubernetes and a major cloud (we run primarily on GCP and AWS).
• Working with LLMs, the right way.
• If you work with LLMs, you work with the model, not only the API: fine-tuning, embedding geometry, retrieval statistics, and transformer internals.
• You can build a RAG system and also explain why the retriever is failing at the embedding level.
• What makes you stand out Face recognition, liveness, or presentation-attack and deepfake detection experience.
• Metric learning and large-scale vector search (millions of vectors and up).
• OCR, document forensics, or tampering detection on real-world degraded documents.
• Contributions to open-source ML, published papers, or teaching and speaking in the community.
• Familiarity with Indian regulatory context: DPDP, RBI KYC modes, UIDAI, PCI DSS.
• This is not that.
• Here you build the models that have real world impact on 2 million people a day, in an adversarial setting where accuracy and cost both have real consequences.
• You get production traffic at 2000 RPS, a fleet of 40+ models to learn from, and a mandate to make them faster, better and cheaper.
• This is a place for people who are still excited by the math