Real data · Updated May 2026

ML Engineer Salary in India (2026): By Experience, City & Company

Machine Learning Engineering is the fastest-growing and highest-paying engineering discipline in India in 2026. Unlike data scientists who focus on model research, ML engineers build the production systems that take models from notebook to scaled inference — covering MLOps pipelines, feature stores, model serving, and LLM integrations. The LLM wave has created an acute shortage of engineers who understand both model internals and production engineering, driving salaries at senior levels to levels comparable with FAANG software engineering.

₹12L
Fresher Median
₹35L
3–5 YoE Median
₹80L
Senior (8+) Median

Key insight: Production-grade ML skills (MLOps, model serving, LLM fine-tuning, feature stores) command 25–40% more than pure data science or research roles. The biggest salary jump in this field is between engineers who prototype models and engineers who deploy and scale them in production.

Key figures at a glance

Junior ML Engineer (0–2 yr)
₹10L – ₹20L (₹14L median)
Mid-level ML Engineer (2–5 yr)
₹22L – ₹55L (₹35L median)
Senior ML Engineer (5–8 yr)
₹45L – ₹95L (₹65L median)
Staff / Principal ML Engineer (8+ yr)
₹75L – ₹130L (₹100L median)

Source: Pathvio salary benchmarks · May 2026 · Annual CTC in INR

ML Engineer Salary by Experience in India

All figures are annual CTC in Indian Rupees. P25 = 25th percentile, Median = 50th, P75 = 75th, P90 = top 10%.

Junior ML Engineer (0–2 yr)
P25₹10L
Median₹14L
P75₹20L
P90₹28L

Python, scikit-learn, basic neural networks, model training pipelines. Usually requires MS/B.Tech in CS + ML coursework.

Mid-level ML Engineer (2–5 yr)
P25₹22L
Median₹35L
P75₹55L
P90₹80L

PyTorch/TensorFlow in production, MLflow, feature engineering, A/B testing ML systems. LLM API integration.

Senior ML Engineer (5–8 yr)
P25₹45L
Median₹65L
P75₹95L
P90₹130L

Full ML system ownership. LLM fine-tuning, RAG systems, distributed training. Cross-team technical leadership.

Staff / Principal ML Engineer (8+ yr)
P25₹75L
Median₹100L
P75₹130L
P90₹160L

ML platform design. Sets model governance and MLOps standards. Often presents to VPs and boards on AI strategy.

ML Engineer Salary by City

City premium applied to median salary. Bangalore commands the highest premium for tech roles in India.

Bangalore+20–25%

India's AI/ML hub. Google DeepMind India, Microsoft Research, Amazon ML, Flipkart AI all headquartered here.

Hyderabad+10–15%

Microsoft India ML centre and Amazon Alexa India drive strong ML demand.

Mumbai+8–12%

Fintech ML (fraud, credit scoring, trading) at Razorpay, Groww, CRED.

Delhi NCRBase

Ed-tech AI (BYJU's, PhysicsWallah) and some startup ML teams.

Pune−10–15%

GCC AI teams but fewer pure-play product ML roles.

Chennai−15–20%

Emerging ML scene; lower bands than Bangalore and Hyderabad.

ML Engineer Salary by Company Type

Company type is the single biggest salary lever in India — often more impactful than years of experience alone.

IT Services (TCS iON, Infosys Nia)
₹8–22LSlow (10–12%/yr)

Applied AI on enterprise datasets. Good for entry-level; limited exposure to cutting-edge ML.

SaaS / B2B AI Product (Sarvam, Krutrim, Ola Krutrim)
₹20–70LVery High

India-specific LLMs and AI products. High equity component; early-stage risk but high upside.

Consumer Tech (Swiggy ML, Zomato AI, PhonePe Fraud)
₹30–100LHigh

Real-time ML at genuine scale — recommendation, fraud, personalisation.

FAANG / Global AI Labs (Google, Meta AI, Microsoft)
₹60–160L+Highest

Research + production ML. PhD preferred for research tracks; MS + strong coding for applied ML.

Skills That Boost Your ML Engineer Salary

Skill premium data based on offer benchmark analysis for India, 2025–26.

LLM Fine-tuning & RAG Systems+35–50%

The most in-demand ML skill of 2026. Engineers who can fine-tune foundation models and build production RAG pipelines are scarce globally, not just in India.

MLOps & Model Serving (Kubeflow, MLflow, Ray Serve)+25–40%

The bridge between research and production. Companies are drowning in models that never ship; MLOps engineers solve this.

PyTorch (deep understanding, not just API usage)+20–30%

Required for senior roles at product companies doing custom model development rather than just API wrappers.

Distributed Training (DDP, FSDP, DeepSpeed)+30–45%

Scarce skill even globally. Needed for teams training or fine-tuning large models.

Feature Store Engineering (Feast, Tecton)+15–25%

Production ML reliability depends on consistent feature pipelines. A known gap in most ML teams.

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