Machine learning engineers at Indian product cos and AI startups build the data pipelines, model training infrastructure, and serving systems behind features like Razorpay fraud detection, Swiggy delivery-time predictions, Flipkart product recommendations, and a wave of GenAI products from Indian startups (Sarvam, Krutrim, Yellow.ai, Glance, BharatGPT-adjacent teams). The work spans Python/PyTorch model code, feature stores on Postgres/Feast, GPU training on AWS/GCP, model serving via Triton/vLLM/SageMaker, MLOps tooling (MLflow, Kubeflow, Vertex AI), and the operational glue around RAG/LLM applications. India-specific patterns include US-export ML platform teams (DeepMind India hires, Google IDC AI, Microsoft Azure AI India, Adobe ML India) that overlap 5-9 PM IST, India-fintech fraud models that handle UPI scale (3B+ transactions a month), and a growing layer of Indic-language NLP work at Sarvam, AI4Bharat, and Krutrim. Compensation skews high — strong ML engineers at product cos (Flipkart, Cred, Razorpay, Swiggy) and FAANG India (Google IDC, Microsoft Research India, Adobe ML) routinely clear 70 lakh to 1.5 crore total comp at the senior level.
Machine learning engineers at Indian product cos and AI startups build the data pipelines, model training infrastructure, and serving systems behind features like Razorpay fraud detection, Swiggy delivery-time predictions, Flipkart product recommendations, and a wave of GenAI products from Indian startups (Sarvam, Krutrim, Yellow.ai, Glance, BharatGPT-adjacent teams). The work spans Python/PyTorch model code, feature stores on Postgres/Feast, GPU training on AWS/GCP, model serving via Triton/vLLM/SageMaker, MLOps tooling (MLflow, Kubeflow, Vertex AI), and the operational glue around RAG/LLM applications. India-specific patterns include US-export ML platform teams (DeepMind India hires, Google IDC AI, Microsoft Azure AI India, Adobe ML India) that overlap 5-9 PM IST, India-fintech fraud models that handle UPI scale (3B+ transactions a month), and a growing layer of Indic-language NLP work at Sarvam, AI4Bharat, and Krutrim. Compensation skews high — strong ML engineers at product cos (Flipkart, Cred, Razorpay, Swiggy) and FAANG India (Google IDC, Microsoft Research India, Adobe ML) routinely clear 70 lakh to 1.5 crore total comp at the senior level.
Check W&B/MLflow for last night's training runs — usually one OOM, one underfit, one promising
Coffee + scan #ml-platform Slack for any feature-store or serving alerts overnight
Standup with the ML pod — data engineering, ML platform, ML researchers — 30 min on Slack Huddle
Deep work: debug a feature-pipeline failure in dbt + Postgres that broke the daily training job
Pair with a junior data engineer on writing a new feature for the fraud-detection feature store (Feast)
Lunch — usually with the ML pod; informal whiteboard discussions about model architecture happen here often
Design review for a new RAG feature — embedding-model choice, retrieval strategy, eval harness; 1 hour with PM and ML staff
Write/review training code for a model variant; kick off a 4-hour GPU run on the internal training cluster
Production debugging — a real-time recommendation model's latency p99 spiked at 2 PM; investigate with the SRE on Datadog
US/EU overlap — review a model card with the US-based PM, align on rollout criteria and the kill-switch threshold
Wrap up — write the model-card update, commit notebooks/scripts, push the PR, log W&B notes
Optional: read a NeurIPS/ICML paper or watch a Karpathy lecture; many Indian ML engineers protect 5-7 hours/week for learning
| City | Range |
|---|---|
| Bangalore | 30-65 LPA |
| Hyderabad | 28-60 LPA |
| Pune | 22-50 LPA |
| NCR (Gurgaon/Noida) | 25-55 LPA |
| Mumbai | 22-50 LPA |
| Remote (US/EU clients) | 60-200 LPA equivalent |
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