MLOps Engineer Salary in India 2026: Why ML Plus Infrastructure Pays

MLOps engineer salary pages for India usually show ranges with no source. We could not find a published, dated source with reliable MLOps pay figures for India, and the Stack Overflow survey has no MLOps role, so this guide does not quote invented numbers. It asks the question that defines the job instead: are people who combine machine learning with infrastructure skills paid more than those who have only one side?

The data is the 2025 Stack Overflow Developer Survey (Stack Overflow survey 2025), with pay converted to US dollars at the 25 June 2025 exchange rate (survey methodology). Each person's pay is divided by the median of peers in the same country and experience band, so 1.00 means paid like a typical peer.

import pandas as pd

# 2025 Stack Overflow Developer Survey: employed respondents who reported annual pay
so = pd.read_csv("results.csv", low_memory=False)
paid = so[so.Employment.eq("Employed") & so.ConvertedCompYearly.gt(0)].dropna(subset=["WorkExp"]).copy()
countries = paid.Country.value_counts()
paid = paid[paid.Country.isin(countries[countries >= 30].index)].copy()
paid["stage"] = pd.cut(paid.WorkExp, [-1, 2, 5, 10, 60], labels=["0-2", "3-5", "6-10", "11+"])
paid["vs_peers"] = paid.ConvertedCompYearly / paid.groupby(["Country", "stage"], observed=True).ConvertedCompYearly.transform("median")
tools = paid.PlatformHaveWorkedWith.fillna("").str.split(";")
paid["docker"] = tools.apply(lambda items: "Docker" in items)
paid["kubernetes"] = tools.apply(lambda items: "Kubernetes" in items)
paid["terraform"] = tools.apply(lambda items: "Terraform" in items)

print("M1 roles that make up MLOps work (median vs same-experience peers, 61 countries)")
for role in ["AI/ML engineer", "DevOps engineer or professional", "Cloud infrastructure engineer", "Data scientist"]:
    group = paid[paid.DevType.eq(role)]
    print(f"   {role:<32} {len(group):>4} people: {group.vs_peers.median():.2f}")

ml = paid[paid.DevType.isin(["AI/ML engineer", "Data scientist"])]
print(f"\nM2 AI/ML engineers and data scientists ({len(ml)}): with and without infrastructure tools")
for tool in ["docker", "kubernetes", "terraform"]:
    print(f"   {tool:<11} users {ml[tool].sum():>3}: {ml[ml[tool]].vs_peers.median():.2f} | others {(~ml[tool]).sum():>3}: {ml[~ml[tool]].vs_peers.median():.2f}")
both = ml.docker & ml.kubernetes
print(f"   share of ML people using both Docker and Kubernetes: {both.mean():.0%}")

india = paid[paid.Country.eq("India")]
print(f"\nM3 India, all {len(india)} employed respondents: with and without infrastructure tools")
for tool in ["docker", "kubernetes", "terraform"]:
    print(f"   {tool:<11} users {india[tool].sum():>3}: {india[india[tool]].vs_peers.median():.2f} | others {(~india[tool]).sum():>3}: {india[~india[tool]].vs_peers.median():.2f}")

M1 roles that make up MLOps work (median vs same-experience peers, 61 countries)
   AI/ML engineer                    287 people: 1.26
   DevOps engineer or professional   603 people: 1.05
   Cloud infrastructure engineer     226 people: 1.16
   Data scientist                    288 people: 1.03

M2 AI/ML engineers and data scientists (575): with and without infrastructure tools
   docker      users 343: 1.20 | others 232: 1.03
   kubernetes  users 142: 1.34 | others 433: 1.09
   terraform   users  70: 1.35 | others 505: 1.12
   share of ML people using both Docker and Kubernetes: 23%

M3 India, all 887 employed respondents: with and without infrastructure tools
   docker      users 379: 1.20 | others 508: 0.90
   kubernetes  users 182: 1.44 | others 705: 0.96
   terraform   users  98: 1.30 | others 789: 1.00

The two halves of MLOps pay differently

MLOps draws on several roles. Against same-experience peers, AI/ML engineers earned 1.26, cloud infrastructure engineers 1.16, DevOps engineers 1.05 and data scientists 1.03. The machine learning side and the infrastructure side both sit above the typical developer, with AI/ML engineering highest.

ML people with infrastructure skills earn more

The clearest result is inside the machine learning group. Among 575 AI/ML engineers and data scientists, those who had used Kubernetes in the past year earned 1.34 times their peers' median, against 1.09 for those who had not. Terraform users earned 1.35 against 1.12, and Docker users 1.20 against 1.03.

Those infrastructure-skilled ML people are not common: only 23% used both Docker and Kubernetes. That combination, a machine learning practitioner who can also package, deploy and run models on real infrastructure, is essentially the MLOps profile, and in this data it was both scarce and better paid.

The same pattern in India

India's sample is too small for ML practitioners alone, but across all 887 employed developers in India the pattern was even stronger. Kubernetes users earned 1.44 times same-experience peers against 0.96 for others; Terraform users 1.30 against 1.00; Docker users 1.20 against 0.90. Infrastructure skills went with clearly higher pay in India, whatever the role.

Reading this carefully

The survey shows association, not cause. Teams that use Kubernetes may be at better-paying employers, and people who learn it may be more experienced within their band. But the direction is the same in every cut: ML practitioners worldwide, and developers in India, with three different tools. That consistency is what makes it worth acting on.

What to do with it

If you come from data science, the gap is infrastructure. Learn to package a model in a container, serve it behind an API, deploy it on Kubernetes and define the environment with infrastructure as code. If you come from DevOps, the gap is the model side: training pipelines, experiment tracking, evaluation and monitoring for drift. Either way, the goal is to be the person who can take a model from a notebook to a reliable service, which is the combination this data rewards.

The MLOps roadmap sets out the order, the data scientist to MLOps engineer roadmap covers the move from the ML side, and Kubernetes for ML is a practical start on the infrastructure side.

A project that proves the combination

Interviewers for MLOps roles want evidence that you can do both halves, and one end-to-end project shows it better than separate exercises.

Start with a modest model on a public dataset; the model itself does not need to be impressive. Track your experiments so you can say which run produced the model you deploy. Package the model with its exact library versions in a container image, serve it behind a small API with a health check, and record a set of golden predictions to compare after every build. Deploy it to a local Kubernetes cluster from a manifest kept in the repository, with resource limits and more than one replica. Define any cloud resources with Terraform, so the whole setup can be destroyed and recreated. Finally, add basic monitoring: request counts, latency and the distribution of predictions, with a note on what would trigger retraining.

Write it up with a diagram and the problems you hit. That one repository demonstrates exactly the overlap this data associates with higher pay.

Pricing an MLOps offer

Titles vary widely: the same work can be called MLOps engineer, ML platform engineer or ML engineer. Compare the responsibilities, not the title, and use local evidence for numbers: published pay bands, the recruiter's band for the level, and people already doing similar work. Compare fixed pay first, then variable pay and any stock with its vesting terms. For the wider AI pay picture, see generative AI salary in India.

Limits of this data

There is no MLOps role in the survey, so ML practitioners stand in for MLOps engineers. The groups using Terraform are small, 70 to 98 people. The India results cover all developers, not ML practitioners. Pay was self-reported and converted on one date, so only ratios are shown.

Packaging, deploying and running models in production is taught in the MLOps course.

Sources

• 2025 Stack Overflow Developer Survey — Stack Overflow
• 2025 Developer Survey methodology (Stack Overflow) — Stack Overflow