MLOps vs DevOps salary in India
Introduction
The salary difference between MLOps and DevOps engineers in India mainly depends on skills, experience, and company type. A DevOps fresher earns around ₹4–9 LPA, while a MLOps fresher starts at ₹6–10 LPA due to added ML and data knowledge. At the mid-level (2–5 years), DevOps professionals earn about ₹8–15 LPA, and MLOps engineers make ₹10–20 LPA. At senior levels (5–10+ years), DevOps salaries reach ₹18–30+ LPA, while MLOps roles often cross ₹20–35+ LPA, especially in AI-focused companies. Overall, MLOps pays 15–30% higher because it combines DevOps automation with machine learning expertise. However, both careers offer strong growth DevOps for cloud and infrastructure lovers, and MLOps for those passionate about AI and data.
1. What Are We Comparing — MLOps Engineer vs DevOps Engineer
1. What Are We Comparing
When we talk about MLOps vs DevOps, both roles sound similar because they share a strong foundation in automation, cloud computing, and continuous integration/continuous delivery (CI/CD).
However, the core focus areas and required skill sets make them distinct, especially when it comes to career growth and salary potential in India.
DevOps Engineer – The Bridge Between Development and Operations
A DevOps Engineer plays a crucial role in managing the software delivery lifecycle — from writing code to deploying and maintaining applications in production.
They build the automation pipelines that help software teams deliver updates faster, with fewer bugs, and more reliability.
Key Responsibilities:
- Designing and maintaining CI/CD pipelines (Continuous Integration and Continuous Deployment).
- Automating repetitive tasks using tools like Jenkins, Terraform, and Ansible.
- Monitoring applications and servers using tools like Prometheus, Grafana, or Datadog.
- Collaborating with developers, QA, and operations teams to ensure smooth product releases.
Goal:
To make software deployment fast, reliable, and automated — ensuring that every code change can move safely to production.
MLOps Engineer – The Bridge Between Data Science and Production
A MLOps Engineer (Machine Learning Operations Engineer) extends DevOps principles into the machine learning and AI world.
While DevOps focuses on software applications, MLOps focuses on machine learning models — automating the entire ML lifecycle from data collection to model deployment.
Key Responsibilities:
- Building end-to-end ML pipelines for data preprocessing, model training, and deployment.
- Managing model versioning and tracking experiments using tools like MLflow, DVC, or Kubeflow.
- Automating model retraining and monitoring to ensure continuous performance improvement.
- Working with data scientists to move research models into production efficiently.
- Handling large-scale data infrastructure using tools like Spark, Hadoop, or Airflow.
Goal:
To operationalize machine learning models — ensuring they run smoothly, are updated regularly, and produce accurate results in real-world environments.
Why MLOps Often Pays More
While both roles require cloud and automation expertise, MLOps adds another layer of complexity — understanding machine learning, data engineering, and AI frameworks.
This multi-disciplinary skill set makes MLOps engineers rarer and, therefore, more valuable in the job market.
- MLOps engineers handle data pipelines + model lifecycle + infrastructure — a combination that very few professionals master.
- DevOps engineers, while critical, are more widely available in the market compared to MLOps specialists.
- As AI adoption grows in industries like finance, healthcare, and e-commerce, demand and salaries for MLOps roles are rising faster.
Difference Between DevOps Engineer & MLOps Engineer
Aspect | DevOps Engineer | MLOps Engineer |
Primary Focus | Software delivery and deployment | Machine Learning model lifecycle |
Key Skills | CI/CD, Cloud, Docker, Kubernetes | ML pipelines, DataOps, Model monitoring |
Tools | Jenkins, Terraform, Ansible, Prometheus | MLflow, Kubeflow, Airflow, TensorFlow |
Team Collaboration | Developers + Operations | Data Scientists + ML Engineers + DevOps |
Goal | Fast & reliable app delivery | Scalable & automated ML deployment |
Salary (India, 2025 avg.) | ₹6 – ₹30 LPA | ₹10 – ₹35+ LPA |
2. Average salary ranges for DevOps in India
3. Average salary ranges for MLOps in India
Experience Level | Approx Annual Salary (LPA) | Key Notes |
Entry / 0-2 yrs | Has baseline of both ML knowledge + infra/devops skills. | |
Mid-level (2-5 yrs) | ~ ₹10 – ₹20 LPA | Roles that require cloud & ML lifecycle tools (MLflow, kubernetes) are in this range. |
Senior / Expert (5+ yrs) | ~ ₹20 – ₹35+ LPA and sometimes higher, particularly with leadership / deep expertise in ML infra. | Also affected by how “ML heavy” the job is, exposure to large scale data, production model deployment, etc. |
Typical / Average across levels |
4. DevOps vs MLOps Salary in India
When comparing DevOps vs MLOps salaries in India, it’s important to look beyond just numbers. Both roles are built on strong automation and cloud foundations — but MLOps combines machine learning and data engineering skills, which makes it more specialized and often better-paid at higher experience levels. Let’s break it down clearly by career stage.
At the Entry Level (0–2 Years of Experience)
At the start of a career, both DevOps and MLOps engineers earn similar salary packages, but a few factors influence who earns slightly more:
DevOps Entry Roles
Average salary: ₹4 – ₹9 LPA.
Typical job titles: Junior DevOps Engineer, Cloud Engineer, Automation Engineer.
Skills in demand: Linux, CI/CD tools (Jenkins, GitHub Actions), Docker, Kubernetes, and cloud basics (AWS/Azure/GCP).
DevOps jobs are more widely available, especially in IT services, startups, and enterprise infrastructure teams.
MLOps Entry Roles
Average salary: ₹6 – ₹10 LPA.
Job titles: Associate MLOps Engineer, ML Infrastructure Engineer.
Skills in demand: Python, MLflow, Airflow, basic data science concepts, cloud platforms, and automation tools.
Companies hiring: AI-based startups, analytics firms, and product companies with ML models in production.
Key Insight:
At this stage, MLOps engineers may earn 10–20% more because of their understanding of machine learning workflows in addition to DevOps skills. However, DevOps engineers have more job openings, making entry easier.
At the Mid-Level (2–5 Years of Experience)
By the mid-career stage, the salary difference becomes more visible. MLOps engineers start earning more because their work directly impacts how ML models perform in real-world systems.
DevOps Mid-Level Roles
Average salary: ₹8 – ₹15 LPA.
Responsibilities: Designing scalable CI/CD pipelines, infrastructure as code (IaC), cloud automation, container orchestration, and monitoring solutions.
Tools: Terraform, Prometheus, Docker, Jenkins, GitOps, AWS/Azure services.
Industries: IT services, fintech, SaaS, e-commerce, healthcare, and telecom.
MLOps Mid-Level Roles
Average salary: ₹10 – ₹20 LPA.
Responsibilities: Building automated ML pipelines, managing data workflows, versioning models, integrating ML APIs, and optimizing training pipelines.
Tools: Kubeflow, MLflow, Airflow, TensorFlow Extended (TFX), Docker, and Kubernetes.
Industries: AI-based startups, data analytics firms, product companies, and research organizations.
Key Insight:
Employers are willing to pay more for MLOps engineers who can combine DevOps expertise with machine learning, since this blend is harder to find. Many DevOps professionals transition to MLOps around this stage for better pay growth.
At the Senior or Expert Level (5+ Years of Experience)
This is where the salary gap between MLOps and DevOps becomes more pronounced, mainly due to specialization and strategic impact on business outcomes.
Senior DevOps Roles
Average salary: ₹18 – ₹33 LPA (sometimes higher in cloud-native or SRE positions).
Job titles: Lead DevOps Engineer, Site Reliability Engineer (SRE), Cloud Architect, DevOps Manager.
Focus areas: Scalable infrastructure design, multi-cloud management, security automation, performance monitoring, and cost optimization.
Senior DevOps professionals in top tech firms (like Google, Amazon, or product-based MNCs) can cross ₹40 LPA.
Senior MLOps Roles
Average salary: ₹20 – ₹35 LPA (and can go beyond ₹40 LPA in AI-first companies).
Job titles: Senior MLOps Engineer, ML Infrastructure Architect, DataOps Lead, Head of ML Systems.
Focus areas: End-to-end ML lifecycle automation, managing data pipelines, scaling models in production, integrating AI solutions into business processes, and leading cross-functional AI teams.
Companies hiring: AI product companies, large analytics consultancies, fintechs, autonomous systems startups, and R&D firms.
Key Insight:
Senior MLOps engineers often earn 10–25% higher salaries than DevOps engineers due to their rare combination of AI, data, and infrastructure skills.
However, top-tier DevOps professionals — especially those working on cloud architecture or platform engineering — can match or exceed MLOps pay, depending on the company and project complexity.
Quick Comparison (2025 Averages)
Experience Level | DevOps Engineer (₹ LPA) | MLOps Engineer (₹ LPA) |
Entry (0–2 yrs) | 4 – 9 | 6 – 10 |
Mid (2–5 yrs) | 8 – 15 | 10 – 20 |
Senior (5–10 yrs) | 18 – 33+ | 20 – 35+ |
Final Verdict
MLOps is a more niche and AI-driven specialization, so the pay scale tends to be higher, especially after a few years of experience.
DevOps remains the foundation for infrastructure automation and is more widely adopted — offering stability, global demand, and career longevity.
Many professionals begin in DevOps and later transition into MLOps once they gain data and ML exposure — leading to faster career and salary growth.
5. Differences Between MLOps v/s DevOps salary in India
The salary difference between MLOps and DevOps engineers in India doesn’t depend on just one thing — it’s a combination of multiple factors like location, company type, skill depth, experience, education, and industry demand.
Let’s explore each of these in detail to understand why some professionals earn more than others, even within the same domain.
1. Location / City
The city you work in plays a huge role in deciding your salary. In India, tech hubs like Bengaluru, Hyderabad, Pune, Chennai, and Delhi-NCR offer the highest packages due to higher cost of living, more startups, and the presence of major MNCs.
High-paying cities for DevOps and MLOps engineers:
Bengaluru – India’s Silicon Valley; salaries can be 25–30% higher than the national average.
Hyderabad – Strong base for cloud, AI, and data companies; demand for MLOps roles is rising.
Pune – Known for IT services and product firms; balanced salary and work environment.
Delhi/NCR – Home to large corporates, fintechs, and AI startups.
Smaller cities like Coimbatore, Jaipur, and Indore offer fewer opportunities and lower salaries — usually 20–40% less compared to Tier-1 cities.
Remote jobs can sometimes bridge this gap if you work for global or pan-India firms that pay based on skills rather than location.
2. Company Type
Different organizations pay differently based on the type of work, innovation level, and technical depth.
Higher-paying company types:
Product-based companies (e.g., Google, Microsoft, Adobe, Zoho): Offer higher salaries and bonuses for automation, cloud, and AI roles.
AI/ML startups: Pay premium salaries for MLOps engineers who can handle full ML pipelines with minimal supervision.
Large Tech MNCs: Offer stable and well-paying roles, especially in DevOps architecture and ML infrastructure.
Lower-paying company types:
IT services & outsourcing firms (e.g., Infosys, Wipro, TCS): Pay relatively lower due to standardized pay scales and higher employee volume.
Non-tech organizations: Firms outside tech (like manufacturing or logistics) may hire DevOps engineers, but usually with lower compensation.
Key takeaway:
MLOps roles in AI-driven product companies or data-focused startups generally offer the highest salary growth potential.
3. Skill Depth
The depth and versatility of your technical skills strongly influence your salary in both MLOps and DevOps careers.
Engineers who master advanced tools and automation systems are rewarded with faster promotions and higher packages.
For DevOps Engineers:
Expertise in cloud platforms (AWS, Azure, GCP) and infrastructure as code (IaC).
Advanced knowledge of containerization (Docker, Kubernetes) and orchestration.
Building end-to-end CI/CD pipelines with tools like Jenkins, GitHub Actions, or GitLab CI.
Skills in monitoring, observability, and security automation using tools like Prometheus, Grafana, and ELK Stack.
For MLOps Engineers:
Knowledge of machine learning lifecycle tools such as MLflow, TFX (TensorFlow Extended), Kubeflow, and Airflow.
Experience with model monitoring, retraining, and version control for ML models.
Handling large-scale data pipelines and real-time model deployment.
Familiarity with data engineering tools like Spark, Kafka, or Snowflake.
Key takeaway:
MLOps engineers typically require a wider skill range (DevOps + Data + ML), which justifies their higher pay at mid and senior levels.
4. Experience Level
Experience is one of the most direct salary drivers. Both DevOps and MLOps engineers earn higher packages as they gain hands-on experience, manage complex systems, and lead teams.
Entry-level (0–2 years):
DevOps: ₹4 – ₹9 LPA
MLOps: ₹6 – ₹10 LPA
Mid-level (2–5 years):
DevOps: ₹8 – ₹15 LPA
MLOps: ₹10 – ₹20 LPA
Senior/Expert (5+ years):
DevOps: ₹18 – ₹33+ LPA
MLOps: ₹20 – ₹35+ LPA
Professionals with leadership experience — such as managing teams, designing architectures, or leading automation initiatives — often see salaries grow 30–50% faster than individual contributors.
5. Certifications & Education
Certifications validate your expertise and directly improve salary potential — especially in technical and cloud-focused roles.
Popular Certifications for DevOps Engineers:
AWS Certified DevOps Engineer – Professional
Microsoft Certified: DevOps Engineer Expert
Docker Certified Associate
Certified Kubernetes Administrator (CKA)
Popular Certifications for MLOps Engineers:
Google Cloud Professional Machine Learning Engineer
AWS Certified Machine Learning – Specialty
TensorFlow Developer Certificate
Databricks Certified Data Engineer / ML Professional
Educational Background:
A Bachelor’s or Master’s degree in Computer Science, IT, Data Science, or AI can increase starting salary offers.
Professionals with dual specialization (like Computer Science + Data Analytics) have stronger earning potential, especially in MLOps.
6. Industry Domain
The industry you work in also determines how much you earn. Certain sectors rely heavily on automation, data, and AI — making them more rewarding for MLOps and DevOps engineers.
High-paying industries:
Finance & FinTech – high demand for model monitoring, fraud detection, and predictive analytics.
E-commerce – strong focus on automation, personalization, and real-time data pipelines.
AI/ML Enterprises & SaaS – pay top salaries for MLOps talent managing production ML models.
Healthcare & Pharma Tech – use ML for diagnostics and data processing.
Moderate to low-paying industries:
Education technology, logistics, or manufacturing may have smaller budgets or rely on external service providers, leading to lower pay scales.
Final Takeaway
Location and skill specialization are the top two factors influencing salary growth.
MLOps roles have a higher skill ceiling — meaning they reward continuous learning in ML and automation.
DevOps roles provide broader job opportunities and global demand, offering strong career stability.
Combining DevOps and MLOps expertise — often called “Full-Stack MLOps” — can position professionals among the top 10% of earners in the Indian tech industry.
6. Sample salary bands
1. DevOps Fresher (₹4 – ₹9 LPA)
Who they are:
Fresh graduates or professionals with less than 2 years of experience who have learned CI/CD, Linux, Git, Jenkins, Docker, and some cloud basics.
Typical roles:
Junior DevOps Engineer
Cloud Operations Associate
Build & Release Engineer
Why the salary range:
₹4–6 LPA: Common in service-based companies or startups.
₹7–9 LPA: Found in product-based firms or companies using advanced DevOps pipelines.
Key skills that boost pay:
AWS/GCP certifications, Kubernetes, Terraform, and real-world CI/CD project experience.
2. MLOps Fresher (₹6 – ₹10 LPA)
Who they are:
Entry-level engineers who combine DevOps knowledge with machine learning basics—like model deployment, data pipelines, and ML workflow automation.
Typical roles:
Junior MLOps Engineer
Data/ML Engineer Intern
AI Deployment Engineer
Why the salary range:
₹6–7 LPA: For those with theoretical ML + DevOps basics.
₹8–10 LPA: For engineers who can build or deploy ML models using tools like MLflow, Kubeflow, or TensorFlow Serving.
Key skills that boost pay:
Python, Docker, AWS Sagemaker, MLflow, and understanding of data preprocessing & model lifecycle.
3. DevOps Mid-Level (2 – 5 Years) (₹6 – ₹12–15 LPA)
Who they are:
Engineers managing CI/CD automation, production deployments, cloud cost optimization, and infrastructure scalability.
Typical roles:
DevOps Engineer
Site Reliability Engineer (SRE)
Infrastructure Automation Specialist
Why the salary range:
₹6–10 LPA: Common in traditional IT firms.
₹12–15 LPA: For experienced DevOps professionals handling microservices, Kubernetes, and IaC (Infrastructure as Code).
Key salary boosters:
Experience with CI/CD pipelines, AWS/GCP/Azure, Docker, Kubernetes, Prometheus, and strong scripting in Bash/Python.
4. MLOps Mid-Level (2 – 5 Years) (₹10 – ₹20 LPA)
Who they are:
Professionals who manage end-to-end ML pipelines—from data collection and training to deployment and monitoring.
Typical roles:
MLOps Engineer
ML Platform Engineer
AI Infrastructure Engineer
Why the salary range:
₹10–15 LPA: For MLOps professionals automating training & deployment pipelines.
₹16–20 LPA: For those working in AI-driven startups, fintech, or product companies with real-time ML systems.
Key salary boosters:
MLflow, Airflow, Docker, Kubernetes, Data Version Control (DVC), Model Monitoring, and experience integrating ML APIs into production.
5. DevOps Senior / Lead (5 – 10+ Years) (₹18 – ₹30+ LPA)
Who they are:
Experts leading DevOps teams, designing high-availability infrastructure, managing cloud budgets, and ensuring zero-downtime deployments.
Typical roles:
Senior DevOps Engineer
DevOps Architect
Cloud Infrastructure Manager
Why the salary range:
₹18–25 LPA: For senior engineers in large enterprises.
₹25–30+ LPA: For leads managing hybrid or multi-cloud environments, and those holding AWS Professional or Architect certifications.
Top salary factors:
Leadership, automation depth, security, cost optimization, and experience with large-scale production systems.
6. MLOps Senior / Lead (5+ Years) (₹20 – ₹35+ LPA)
Who they are:
Professionals who not only automate ML workflows but also understand data pipelines, AI model governance, version control, and real-time inference systems.
Typical roles:
Senior MLOps Engineer
ML Infrastructure Architect
Head of AI Platform
Why the salary range:
₹20–30 LPA: For experienced engineers managing ML models in production.
₹30–35+ LPA: For those in AI-first companies (FinTech, HealthTech, or product startups using advanced AI).
Key salary boosters:
Advanced ML workflow design, cloud ML platforms (AWS Sagemaker, Azure ML, GCP Vertex AI), data engineering expertise, and collaboration with data scientists.
Summary – MLOps vs DevOps Salary in India
Level | DevOps Salary | MLOps Salary |
Fresher | ₹4–9 LPA | ₹6–10 LPA |
Mid-Level (2–5 yrs) | ₹6–15 LPA | ₹10–20 LPA |
Senior / Lead (5+ yrs) | ₹18–30+ LPA | ₹20–35+ LPA |
7. What This Means / Key Insights
1. MLOps Offers Higher Long-Term Salary Growth
If your goal is to reach higher pay brackets in the next few years, MLOps is the stronger path. The reason is simple:
MLOps engineers need to know everything a DevOps engineer does — plus they work with machine learning models, data pipelines, and AI systems.
This combination of DevOps + ML knowledge makes them more specialized and valuable to companies building AI-driven products.
As a result, once you move beyond the fresher level, MLOps professionals can expect 20–40% higher pay hikes compared to traditional DevOps roles.
Example:
A mid-level DevOps engineer might earn ₹10–12 LPA, while a similar-level MLOps engineer can earn ₹15–20 LPA because they manage both infrastructure and ML automation.
2. DevOps is Still Highly Valuable and Rewarding
It’s important to understand that DevOps isn’t a “lesser” field.
Many companies depend heavily on DevOps for their cloud infrastructure, automation, and deployment strategies.
DevOps engineers with strong skills in cloud architecture, Kubernetes, Docker, and CI/CD can easily reach ₹30 LPA or more in top organizations.
Roles like Site Reliability Engineer (SRE) or Cloud Infrastructure Architect often offer salaries on par with MLOps jobs.
Key point:
If your interest is in automation, scalability, and system reliability, DevOps is still a brilliant and high-paying career option.
3. MLOps is a Differentiator for the Future
If you’re just starting out, learning MLOps can give you a strong competitive edge in the coming years.
Every large company (e.g., Google, Amazon, Infosys, TCS, and AI startups) is now investing in AI-powered systems — which require engineers who understand both ML and DevOps.
Skills like MLflow, Airflow, TensorFlow Serving, and AWS SageMaker are becoming core parts of the new tech stack.
So, combining your DevOps foundation with AI/ML tools and data workflow automation can help you stand out and secure higher-paying, future-proof roles.
Bottom line:
If you’re comfortable with Python, data handling, and automation, MLOps can fast-track your growth.
4. Non-Salary Benefits Are Stronger in AI/MLOps Roles
Apart from salary, MLOps and AI-focused companies often offer better overall packages:
Stock options (ESOPs): Common in AI startups and product-based firms.
Remote or hybrid work: Many AI and data-driven teams work remotely.
Career growth: MLOps engineers often transition into Machine Learning Engineers, AI Platform Architects, or AI Product Managers over time.
Cross-functional exposure: You’ll collaborate with data scientists, AI researchers, and cloud architects — giving you diverse experience.
In contrast, DevOps roles in traditional IT or cloud management may offer less exposure to AI systems but provide stability, consistent work, and clear technical paths.
5. Final Insight — Choose Based on Interest, Not Just Salary
While salary is important, your long-term success depends on your
interest and comfort zone:
If you enjoy automation, deployment pipelines, and infrastructure management, go for DevOps.
If you are curious about AI models, data pipelines, and ML workflows, start learning MLOps gradually — it’s the future of intelligent automation.
Tip:
You can start as a DevOps engineer, build strong cloud and automation skills, and later transition into MLOps by adding machine learning and data engineering knowledge.
In Summary
Focus Area | Growth | Pay Scale | Future Demand | Ideal For |
DevOps | High | ₹6–30+ LPA | Stable & in-demand | Cloud, CI/CD, SRE enthusiasts |
MLOps | Very High | ₹8–35+ LPA | Rapidly growing | AI/ML + automation learners |
Conclusion
In India, both DevOps and MLOps are high-demand, well-paying careers. MLOps generally offers higher salaries (15–30% more) because it combines DevOps automation with machine learning and AI expertise. DevOps remains highly valuable, especially in cloud, CI/CD, and infrastructure roles. Your choice should depend on interest and skills: choose DevOps if you enjoy system reliability and automation, or MLOps if you are passionate about AI, data pipelines, and ML model deployment. Both paths provide strong growth, career stability, and future opportunities in India’s booming tech industry.
FAQs
What is the average salary of a DevOps engineer in India?
- Entry-level: ₹4–9 LPA
- Mid-level (2–5 yrs): ₹8–15 LPA
- Senior/Lead (5+ yrs): ₹18–30+ LPA
What is the average salary of an MLOps engineer in India?
- Entry-level: ₹6–10 LPA
- Mid-level (2–5 yrs): ₹10–20 LPA
- Senior/Lead (5+ yrs): ₹20–35+ LPA or higher in AI-first companies
Why does MLOps pay more than DevOps?
- MLOps combines DevOps skills with machine learning and AI knowledge, which is rarer and in high demand.
Are DevOps engineers in India well-paid?
- Yes, especially in cloud, SRE, and infrastructure roles. Senior DevOps salaries can match MLOps in top tech companies.
Which skills increase DevOps salary in India?
- Cloud platforms (AWS, Azure, GCP), CI/CD pipelines, Kubernetes, Docker, Terraform, and automation expertise.
Which skills increase MLOps salary in India?
- ML pipelines, model monitoring, data versioning, Kubeflow, MLflow, Airflow, and experience with AI/ML model deployment.
Does location affect salary?
- Yes. Cities like Bangalore, Hyderabad, Pune, and Delhi NCR offer higher salaries than smaller cities.
Do certifications impact salary?
- Yes. AWS, Azure, GCP, Kubernetes, MLflow, TensorFlow, and AI certifications can significantly increase pay.
Is MLOps suitable for beginners?
- Yes, if you have some Python and basic ML knowledge. Starting in DevOps first can also help transition to MLOps.
Can a DevOps engineer move into MLOps?
- Absolutely Learning ML pipelines, data workflows, and model deployment can help a DevOps engineer transition into MLOps roles.
- Absolutely Learning ML pipelines, data workflows, and model deployment can help a DevOps engineer transition into MLOps roles.