MLOPS Engineer Salary In India
Introduction
MLOps Engineer salaries in India vary significantly with experience, ranging from approximately ₹6–10 lakhs per annum (LPA) for entry-level roles to ₹20–35 LPA or more for senior-level positions with specialized skills.
The average salary across India is around ₹12–18 LPA, though this can be much higher in major tech hubs like Bangalore, Hyderabad, and Pune, where demand is strong.
Salary Breakdown by Experience
- Entry-Level (0–2 years): Expect salaries in the range of ₹6 to ₹10 LPA.
- Mid-Level (2–5 years): Salaries usually fall between ₹10 and ₹20 LPA.
- Senior-Level (5+ years): With experience in cloud platforms, CI/CD, and advanced ML tools, salaries can be ₹20–35 LPA or even higher.
Key Factors Influencing Salary
- Experience
More years of experience and specialized expertise command higher salaries. - Skills
Proficiency in cloud platforms (AWS, GCP, Azure), CI/CD pipelines, containerization (Docker, Kubernetes), and machine learning tools is highly valued. - Location
Tech hubs like Bangalore, Delhi NCR, Mumbai, and Hyderabad often offer higher salaries due to demand.
Company:
Salaries differ between multinational corporations, startups, and different industries (finance, healthcare, IT services, etc.).
What is MLOps, and Who is an MLOps Engineer?
Before talking about salaries, let us first understand what MLOps is and what an MLOps Engineer does.
- MLOps = Machine Learning + Operations.
- It is the process of building, deploying, and maintaining machine learning models in production.
- Just like DevOps manages software systems, MLOps ensures AI/ML models work smoothly after development.
Who is an MLOps Engineer?
An MLOps Engineer is a professional who works at the intersection of data science, machine learning, and DevOps.
Their main goal is to deploy ML models into production environments and keep them running efficiently.
Think of it this way:
- A Data Scientist builds the model.
- An MLOps Engineer makes sure that model runs 24/7, at scale, without errors.
Roles and Responsibilities of an MLOps Engineer
- Automating ML workflows (training → testing → deployment).
- Building pipelines for continuous integration and delivery (CI/CD).
- Managing cloud infrastructure (AWS, Azure, GCP).
- Monitoring model performance and fixing errors.
- Ensuring security, scalability, and cost efficiency of ML systems.
In short: MLOps Engineers are the bridge between data scientists and IT operations teams.
Role of an MLOps Engineer
The role of an MLOps Engineer has grown very important in recent years. As more companies adopt AI and machine learning, the need for someone to deploy, manage, and maintain ML models has become critical. This is exactly what an MLOps Engineer does.
Unlike traditional software engineers, who only work with applications, an MLOps Engineer works with machine learning models that are constantly evolving. They make sure these models can handle real-world data and run smoothly at scale.
Day-to-Day Responsibilities
Here are some common tasks that an MLOps Engineer handles daily:
- Model Deployment: Taking machine learning models built by data scientists and deploying them into production.
- Automation: Creating automated pipelines so models can be retrained and updated without manual effort.
- Monitoring: Checking the health of ML models (accuracy, speed, and performance).
- Scaling: Ensuring the model can handle large amounts of data and millions of user requests.
- Collaboration: Working closely with data scientists, developers, and IT operations teams.
- Cost Optimization: Managing cloud costs and making ML systems efficient.
MLOps Engineer vs Data Scientist
Many people confuse MLOps Engineers with Data Scientists, but they are different.
- Data Scientist
Focuses on research, building ML models, and improving algorithms.
Example: A data scientist builds a model to predict customer churn. - MLOps Engineer
Ensures that the churn prediction model is deployed, monitored, updated, and reliable in real-world use.
Example: An MLOps engineer sets up the pipeline so the churn model updates automatically when new customer data arrives.
In simple terms: Data Scientists build the car, MLOps Engineers keep it running on the road.
Why This Role is Important
- Companies cannot rely only on data scientists.
- Without proper deployment, even the best ML models are useless.
- Businesses need MLOps Engineers to make AI usable for customers.
This high demand is one of the main reasons why salaries for MLOps Engineers are rising so fast in India.
Skills Required for MLOps Engineers
To become a successful MLOps Engineer in India (and to earn higher salaries), you need a mix of technical skills, tool knowledge, and soft skills.
Stronger skills directly increase your demand and worth in the job market.
A. Technical Skills
- Programming Languages
- Python: The most widely used language in ML and MLOps.
- R, Java, or Scala: Useful in some industries, but Python dominates.
- Example: Writing Python scripts to automate model training.
- Python: The most widely used language in ML and MLOps.
- Machine Learning Basics
- Knowing the process of creating, training, and testing ML models to ensure they work correctly.
- Example: Knowing how to check if a model is overfitting or performing poorly.
- Data Handling
- Skills in SQL, Pandas, and NumPy to handle large datasets.
- Example: Preparing real-time customer transaction data for fraud detection models.
- Skills in SQL, Pandas, and NumPy to handle large datasets.
- DevOps Knowledge
- CI/CD pipelines → Continuous Integration and Continuous Deployment.
- Example: Automating the process so a model update goes live in production without human effort.
- CI/CD pipelines → Continuous Integration and Continuous Deployment.
- Cloud Platforms
- AWS, Google Cloud (GCP), and Microsoft Azure are highly in demand.
- Example: Deploying ML models using AWS SageMaker or Google Vertex AI.
- AWS, Google Cloud (GCP), and Microsoft Azure are highly in demand.
- Containerization and Orchestration
- Docker: Package ML models into containers.
- Kubernetes: Scale those containers across servers.
- Example: Running an image recognition model inside Docker and scaling it with Kubernetes to handle millions of app users.
- Docker: Package ML models into containers.
- Monitoring and Logging Tools
- Prometheus, Grafana, ELK Stack
- Example: Monitoring the accuracy of a recommendation model for an e-commerce website.
- Prometheus, Grafana, ELK Stack
B. MLOps Tools and Technologies
Some popular MLOps tools you should know are:
- MLflow → For tracking experiments, model versioning, and deployment.
- Kubeflow → For running ML workflows on Kubernetes.
- TensorFlow Extended (TFX) → A Google framework designed to build and manage ML pipelines in real-world production systems.
- Airflow → Automates workflows and scheduling.
- DVC (Data Version Control) → Helps manage datasets and models like GitHub manages code.
Example: A company like Flipkart may use MLflow to track product recommendation models while using Airflow to schedule daily retraining jobs.
C. Soft Skills
Technical knowledge is not enough. MLOps Engineers also need soft skills:
- Problem-solving → Debugging errors in ML pipelines.
- Teamwork → Collaborating with data scientists, software engineers, and business teams.
- Communication → Explaining complex ML operations in simple terms.
- Adaptability → Learning new tools quickly as technology keeps evolving.
D. Why Skills Matter for Salary
- A fresher with only Python knowledge may start at ₹6–8 LPA.
- But someone with cloud + Docker + MLflow + Kubernetes can command ₹15–20 LPA even with 2–3 years of experience.
Senior engineers with expertise in multiple cloud platforms and end-to-end ML pipeline design can earn ₹30+ LPA in India.
Factors Influencing MLOps Engineer Salary in India
Not every MLOps Engineer earns the same salary. Pay depends on education, experience, skills, location, industry, and company type. Let’s break it down.
A. Education and Qualifications
- A Bachelor’s degree in Computer Science, IT, or related fields is usually the minimum requirement.
- Master’s degrees (M.Tech, MS, MBA with AI specialization) can give an edge, especially in top companies.
- Professionals with certifications in cloud (AWS, GCP, Azure) or MLOps tools often get paid more.
Example
- A B.Tech graduate starting in a small company may earn ₹6 LPA.
- The same candidate with an AWS certification could get ₹8–10 LPA.
B. Experience Level
- Freshers (0–2 years): Usually work under senior engineers, focusing on simple tasks like model deployment.
- Mid-level (2–5 years): Manage pipelines, monitor models, and optimize workflows.
- Senior (5+ years): Handle architecture design, mentor teams, and manage large-scale ML systems.
Hands-on experience plays the most important role in boosting an MLOps Engineer’s salary.
C. Location (City)
Salaries vary a lot depending on the city you work in:
- Bangalore: Highest pay due to being India’s tech hub.
- Hyderabad, Pune, Delhi NCR: Competitive salaries, especially in IT services and startups.
- Mumbai: Higher pay in the finance and banking industries.
- Smaller cities: Lower salaries due to limited demand.
D. Industry
Some industries pay more because ML is critical for their business:
- Finance and Banking → Fraud detection, credit scoring.
- Healthcare → AI-driven diagnosis, medical imaging.
- E-commerce → Recommendation systems, demand forecasting.
- IT Services → Outsourced MLOps projects.
- Startups → May pay less initially but offer faster growth and stock options.
E. Company Size
- Multinational Corporations (MNCs): Pay higher, offer stability.
- Startups: Pay may be lower initially, but growth can be faster.
- Product-based companies: Usually pay the highest because ML models directly impact their revenue.
F. Skills and Tools
The more tools you know, the better your salary.
- Basic skills (Python, SQL, ML basics): ₹6–8 LPA.
- Intermediate (Cloud, Docker, CI/CD): ₹10–15 LPA.
- Advanced (Kubernetes, MLflow, TFX, end-to-end pipeline design): ₹20–35 LPA.
MLOps Engineer Salary Comparison in India (2025)
Level | Experience | Average Salary (₹ LPA) | Key Skills Expected |
Entry-Level | 0–2 years | ₹6 – ₹10 LPA | Python, ML basics, SQL, Git |
Mid-Level | 2–5 years | ₹10 – ₹20 LPA | Cloud (AWS/GCP/Azure), Docker, CI/CD, MLflow |
Senior-Level | 5+ years | ₹20 – ₹35+ LPA | Kubernetes, advanced MLOps pipelines, monitoring tools, and leadership skills |
Tip: Salaries are not fixed. A fresher with only Python may earn ₹6 LPA, but a fresher with cloud + Docker + MLflow could easily earn ₹10–12 LPA in Bangalore or Hyderabad.
Average Salary of MLOps Engineers in India (2025)
MLOps Engineer salaries in India have seen a strong rise in the last 3–4 years. The demand for professionals who can deploy and maintain ML models in production is at an all-time high.
Based on reports from Glassdoor, AmbitionBox, and LinkedIn (2025), here’s the current salary range
- Overall Average: ₹12 – ₹18 LPA
- Lowest Range (Freshers): ₹6 – ₹8 LPA
- Highest Range (Senior/Experts): ₹30 – ₹35+ LPA
Salary by Experience Level
- Entry-Level (0–2 years)
- Salary: ₹6 – ₹10 LPA
- Example: A fresher in a service company may earn ₹6.5 LPA, while in a Bangalore startup, it could be ₹9 LPA.
- Salary: ₹6 – ₹10 LPA
- Mid-Level (2–5 years)
- Salary: ₹10 – ₹20 LPA
- Example: An engineer with AWS + Docker + MLflow skills can easily command ₹15 LPA.
- Salary: ₹10 – ₹20 LPA
- Senior-Level (5+ years)
- Salary: ₹20 – ₹35 LPA
- Example: A lead MLOps Engineer in a product company like Amazon or Microsoft can earn above ₹30 LPA.
- Salary: ₹20 – ₹35 LPA
City-Wise Salary Breakdown in India (2025)
City | Average Salary (₹ LPA) | Salary Range (₹ LPA) | Notes |
Bangalore | ₹15 – ₹20 LPA | ₹8 – ₹35 LPA | Highest salaries, hub for startups & MNCs |
Hyderabad | ₹12 – ₹18 LPA | ₹7 – ₹30 LPA | Strong IT service demand |
Pune | ₹11 – ₹16 LPA | ₹6 – ₹25 LPA | Popular for product & IT companies |
Chennai | ₹10 – ₹15 LPA | ₹6 – ₹22 LPA | Growing AI adoption |
Delhi NCR | ₹12 – ₹17 LPA | ₹7 – ₹28 LPA | Mix of finance + IT jobs |
Mumbai | ₹11 – ₹16 LPA | ₹7 – ₹26 LPA | Higher salaries in the BFSI sector |
Tier-2 Cities (Jaipur, Kochi, Ahmedabad, etc.) | ₹7 – ₹12 LPA | ₹5 – ₹15 LPA | Fewer opportunities, lower salaries |
Insight: Bangalore is the clear leader. Even a fresher with the right skills can start at ₹9–10 LPA here, compared to ₹6–7 LPA in smaller cities.
Salary Growth Trend in 2025
- Salaries have grown by 20–30% in the last 2 years.
- With more companies adopting AI, the average pay is expected to increase further in the next 3–5 years.
- Experts predict that senior engineers could cross ₹40 LPA in top MNCs by 2030.
Salary Comparison with Other Tech Roles
MLOps Engineers are an essential part of today’s rapidly growing AI-driven workforce. But how do their salaries compare with other popular tech roles like Data Scientists, Software Engineers, and DevOps Engineers?
Let’s break it down.
MLOps Engineer vs Data Scientist
- A Data Scientist focuses on building models.
- An MLOps Engineer focuses on deploying and maintaining models in production.
- Salaries are becoming similar, but MLOps is now slightly higher because it requires DevOps + ML knowledge.
MLOps Engineer vs Software Engineer
- Software Engineers mainly develop applications.
- MLOps Engineers specialize in managing ML models, building pipelines, and handling cloud-based infrastructure.
- Since MLOps requires specialized AI/ML + DevOps skills, salaries are 10–20% higher on average.
MLOps Engineer vs DevOps Engineer
- DevOps Engineers handle application pipelines.
- MLOps Engineers take on similar responsibilities, but their focus is on machine learning workflows and models.
- Salaries for MLOps are slightly higher because AI/ML knowledge adds value.
Salary Comparison Table (India – 2025)
Role | Average Salary (₹ LPA) | Entry-Level (₹ LPA) | Senior-Level (₹ LPA) |
MLOps Engineer | ₹12 – ₹18 LPA | ₹6 – ₹10 LPA | ₹20 – ₹35+ LPA |
Data Scientist | ₹11 – ₹16 LPA | ₹5 – ₹9 LPA | ₹18 – ₹30 LPA |
Software Engineer | ₹8 – ₹14 LPA | ₹4 – ₹7 LPA | ₹15 – ₹25 LPA |
DevOps Engineer | ₹10 – ₹15 LPA | ₹6 – ₹9 LPA | ₹18 – ₹28 LPA |
Key Takeaways
- MLOps Engineers earn slightly higher salaries than both Data Scientists and DevOps Engineers.
- Software Engineers generally earn less unless they move into AI/ML-related domains.
As AI adoption grows, MLOps Engineers will continue to outpace other roles in demand and pay.
Regional Salary Variations in India
MLOps Engineer salaries in India vary widely depending on the location.
It depends a lot on the city, industry, and type of company.
Let’s look at the city-wise breakdown with some company examples.
1. Bangalore – The Silicon Valley of India
- Known as India’s tech capital.
- Home to startups, IT giants, and AI labs.
- Salaries here are the highest in India.
- Average Salary: ₹14 – ₹20 LPA
- Top Employers: Google, Microsoft, Infosys, Flipkart, Swiggy, Ola.
2. Hyderabad – AI & Cloud Hub
- Major hub for AI, cloud, and data companies.
- Many global firms like Amazon, Microsoft, and Apple have offices here.
- Salaries are almost equal to Bangalore.
- Average Salary: ₹13 – ₹18 LPA
- Top Employers: Amazon, Microsoft, TCS, Tech Mahindra, Deloitte.
3. Pune – IT & FinTech Stronghold
- Known for IT services and banking/fintech companies.
- Salaries are slightly lower than Bangalore/Hyderabad.
- Average Salary: ₹10 – ₹15 LPA
- Top Employers: Infosys, Wipro, Barclays, Mastercard, Zensar.
4. Chennai – Enterprise IT Market
- Strong in IT services and enterprise software.
- Salaries are moderate, but the cost of living is lower.
- Average Salary: ₹9 – ₹14 LPA
- Top Employers: Cognizant, TCS, Accenture, Zoho, Freshworks.
5. Delhi NCR (Gurgaon & Noida) – Corporate Tech Hub
- Home to corporates, e-commerce, and consulting firms.
- Salaries are competitive, especially in Gurgaon.
- Average Salary: ₹11 – ₹16 LPA
- Top Employers: Paytm, Zomato, OYO, IBM, HCL, EY.
6. Other Cities (Kolkata, Ahmedabad, Kochi, etc.)
- Smaller IT hubs with growing opportunities.
- Salaries are lower compared to metro cities.
- Average Salary: ₹7 – ₹11 LPA
- Top Employers: TCS, Wipro, Infosys (service centers).
Quick City-Wise Salary Snapshot
City/Region | Average Salary (₹ LPA) | Top Employers |
Bangalore | ₹14 – ₹20 LPA | Google, Flipkart, Infosys |
Hyderabad | ₹13 – ₹18 LPA | Amazon, Microsoft, Deloitte |
Pune | ₹10 – ₹15 LPA | Infosys, Barclays, Mastercard |
Chennai | ₹9 – ₹14 LPA | Cognizant, Zoho, Freshworks |
Delhi NCR | ₹11 – ₹16 LPA | Paytm, IBM, HCL |
Other Cities | ₹7 – ₹11 LPA | TCS, Wipro, Infosys |
International Salary Insights for MLOps Engineers
The salary of an MLOps Engineer varies a lot across countries depending on demand, living costs, and industry adoption of AI.
Here’s a clear country-wise comparison
1. United States (Highest Demand)
- Entry-Level: $90,000 – $110,000 per year
- Mid-Level: $120,000 – $150,000 per year
- Senior-Level: $160,000 – $200,000+ per year
- Top Employers: Google, Amazon, Microsoft, Netflix, Nvidia
2. United Kingdom
- Entry-Level: £45,000 – £60,000 per year
- Mid-Level: £65,000 – £85,000 per year
- Senior-Level: £90,000 – £120,000+ per year
- Top Employers: DeepMind, BBC, HSBC, Accenture, Capgemini
3. Canada
- Entry-Level: CAD $75,000 – $90,000 per year
- Mid-Level: CAD $95,000 – $120,000 per year
- Senior-Level: CAD $125,000 – $160,000+ per year
- Top Employers: Shopify, RBC, Scotiabank, CGI, IBM Canada
4. Germany (Europe’s AI Hub)
- Entry-Level: €55,000 – €70,000 per year
- Mid-Level: €75,000 – €95,000 per year
- Senior-Level: €100,000 – €130,000+ per year
- Top Employers: Siemens, SAP, BMW, Bosch, Mercedes-Benz
5. Middle East (UAE, Saudi Arabia, Qatar)
- Entry-Level: $45,000 – $65,000 per year
- Mid-Level: $70,000 – $100,000 per year
- Senior-Level: $110,000 – $140,000+ per year
- Top Employers: Emirates Group, Aramco, Etisalat, Deloitte ME
International Salary Snapshot (Annual, Average Ranges)
Country | Entry-Level | Mid-Level | Senior-Level | Top Employers |
USA | $90k – $110k | $120k – $150k | $160k – $200k+ | Google, Amazon, Nvidia |
UK | £45k – £60k | £65k – £85k | £90k – £120k+ | DeepMind, HSBC |
Canada | CAD 75k – 90k | CAD 95k – 120k | CAD 125k – 160k+ | Shopify, RBC |
Germany | €55k – €70k | €75k – €95k | €100k – €130k+ | SAP, BMW |
Middle East | $45k – $65k | $70k – $100k | $110k – $140k+ | Emirates, Aramco |
Key Insight:
MLOps Engineers in the US and Germany earn the highest salaries globally, while the Middle East offers tax-free salaries with additional perks (housing, travel, bonuses).
Future Trends & Career Growth in MLOps Salaries
The demand for MLOps Engineers is growing rapidly because companies are shifting from just building AI models to deploying and scaling them in production. This trend will directly impact salary growth in India and worldwide.
1. Explosive AI Adoption
- By 2030, AI is projected to contribute around $15.7 trillion to the global economy.
- Every industry (healthcare, finance, retail, manufacturing, defense) will need MLOps Engineers to run AI smoothly.
- Salaries will increase due to a shortage of skilled professionals.
2. Cloud & Generative AI Boom
- Tools like AWS Sagemaker, Azure ML, and Vertex AI are making MLOps central to AI workflows.
- Generative AI (ChatGPT, Claude, MidJourney) creates new MLOps challenges → more job opportunities.
- Companies will pay premium salaries for engineers skilled in cloud + GenAI + automation.
3. Salary Predictions (India)
- 2025 (Current): ₹6 LPA – ₹25 LPA, based on experience level.
- 2027: ₹10 LPA – ₹35 LPA (more MNCs adopting GenAI).
- 2030: ₹15 LPA – ₹50 LPA (MLOps becomes a must-have in every AI team).
4. Salary Predictions (Global)
- USA → From $120k average today → $180k–$220k by 2030.
- Europe → From €85k average today → €120k–€150k by 2030.
- Middle East → More AI investment → salaries up by 40–50% in next 5 years.
5. Job Security & Growth
- MLOps Engineers are future-proof because:
- AI will always need deployment pipelines.
- Companies want cost savings + efficiency → automation grows.
- The role blends AI + DevOps + Cloud, making it harder to replace.
- AI will always need deployment pipelines.
Future Salary Outlook (India + Global)
Year | India (Avg. Salary) | USA (Avg. Salary) | Europe (Avg. Salary) | Middle East (Avg. Salary) |
2025 | ₹6–25 LPA | $120k | €85k | $80k |
2027 | ₹10–35 LPA | $150k | €110k | $100k |
2030 | ₹15–50 LPA | $180k–220k | €120k–150k | $120k+ |
Key Takeaway:
If you start learning MLOps now, you’ll be in the top 5% of highly paid engineers within the next 5 years, as the demand-supply gap will widen massively.
How to Increase Your Salary as an MLOps Engineer (Step-by-step Roadmap)
Below is a simple, clear career roadmap that helps increase pay.
Year 0–1: Learn the basics
- Skills to learn: Python, ML basics, SQL, Git.
- Small projects: Deploy a model on a small cloud VM. Make a simple REST API for predictions.
- Goal: Build 1–2 small projects that show you can deploy models.
Year 1–3: Become productive in MLOps tools
- Skills to add: Docker, simple CI/CD, basic AWS or GCP.
- Tools: MLflow for tracking, Airflow for scheduling.
- Projects: Create a full MLOps pipeline that handles data ingestion, trains the model, deploys it, and sets up monitoring.
- Outcome: Progress from entry-level to mid-level roles and earn around ₹10–15 LPA at the right company.
Year 3–6: Specialize & lead
- Skills to learn: Kubernetes, distributed model training, and advanced monitoring using Prometheus and Grafana.
- Architecture: Design scalable MLOps architectures, optimize cost.
- Certifications: AWS/GCP professional certs, Kubernetes (CKA).
- Outcome: Senior roles, ₹20–35 LPA in product companies.
6+ years: Architect & manage
- Skills to add: System design for ML, leadership, and cross-team communication.
Outcome: Lead or architect roles, total comp often above ₹35–40 LPA in top firms.
Practical Career Tips (Simple & Direct)
- Build projects that show production skills. Code in a repo with a README and deployment steps.
- Learn cloud fundamentals. Even one provider (AWS or GCP) is enough at the start.
- Know Docker + Kubernetes basics. Companies expect this.
- Use real data. Public datasets + pipelines prove you can handle messier inputs.
- Add monitoring and retraining logic. That’s the core of MLOps.
- Get certifications only if they support job goals (like AWS/GCP/Kubernetes).
- Network & apply broadly. Many jobs aren’t advertised; referrals help.
Be ready to explain trade-offs: accuracy vs cost, latency vs batch processing.
How to Negotiate Salary (Simple Steps)
- Know market ranges for your level (use Glassdoor, LinkedIn, Payscale).
- Show impact: “I reduced model cost by X%” or “I improved latency from Y ms to Z ms.”
- Ask for a range, not a single number. Employers often have flexibility.
- Look at the full compensation package — base pay, bonuses, stock/options, and additional perks.
- If the counteroffer is low, ask about role growth and the timeline for a raise.
Sample Resume Bullet Points (MLOps)
Use these to show measurable impact
- “Built CI/CD pipeline for model training and deployment using GitHub Actions and Docker; reduced deployment time from 3 hours to 15 minutes.”
- “Implemented model monitoring with Prometheus and Grafana, identified data drift, and set up automated rollback.”
- “Migrated batch training job to AWS SageMaker, cutting cloud costs by 30%.”
“Led cross-functional team of 4 to productionize recommendation model serving 5M users daily.”
Interview Prep (What Employers Ask)
Common topics
- System design for ML (how to deploy, scale, and monitor a model).
- Containerization + Kubernetes questions.
- Cloud architecture questions (SageMaker/Vertex examples).
- Coding: Python, data handling (Pandas, SQL).
- Troubleshooting and debugging real incidents.
Practice: Build a small end-to-end project and be ready to explain decisions.
Conclusion & Career Advice
MLOps is emerging as one of the most exciting and high-growth career paths in the tech industry today. It sits at the sweet spot between machine learning and software operations. If you build the right skills — cloud, containers, CI/CD, monitoring — and gain production experience, you can move quickly from entry-level pay (₹6–10 LPA) to mid and senior levels (₹15–35+ LPA) in a few years. Top product companies pay even more for architects and leads.
Final tips
- Start with small production projects.
- Learn one cloud provider well.
- Focus on measurable impact (cost, latency, accuracy).
- Keep learning — MLOps tools change fast.
(According to 2025 data from Glassdoor and PayScale, MLOps positions in India typically offer salaries between ₹12 and ₹18 LPA, with Bengaluru and Hyderabad providing above-average compensation, while senior-level roles can surpass ₹30 LPA).
FAQs
The average salary of an MLOps Engineer in India is around ₹12–18 LPA. Entry-level roles start at about ₹6–10 LPA, while senior experts can earn ₹20–35 LPA or even more in top companies.
Freshers with 0–2 years of experience usually earn between ₹6–10 LPA. The salary depends on skills in cloud, automation, and ML tools, as well as the company size.
Senior MLOps Engineers with 8+ years of experience can earn ₹35 LPA or more, especially in Bangalore, Delhi NCR, or with global tech firms like Google, Microsoft, and Amazon.
In many cases, yes. While Data Scientists earn around ₹10–20 LPA, MLOps Engineers often get higher pay (₹12–25 LPA on average) because of their specialized skills in automation, deployment, and cloud platforms.
Skills in cloud platforms (AWS, Azure, GCP), CI/CD pipelines, Docker, Kubernetes, Python, ML frameworks, and monitoring tools can significantly increase salary. The more advanced and rare your skills, the higher your pay.
Bangalore is the top-paying city, offering 15–25% more salary compared to other cities. Delhi NCR, Hyderabad, Pune, and Mumbai also offer strong packages for skilled professionals.
MNCs usually pay more stable salaries with benefits. However, startups can also offer competitive packages, stock options, and faster career growth, especially if they are AI-first companies.
Yes, MLOps is in high demand as companies adopt AI and machine learning. Demand is growing rapidly because businesses need professionals to deploy, monitor, and scale ML models in production.
Yes, but it requires learning key skills like Linux, Python, cloud computing, and ML basics. Most freshers start as DevOps or ML Engineers and then specialize in MLOps within 1–2 years.
Experience plays a huge role.
- 0–2 years: ₹6–10 LPA
- 2–5 years: ₹10–20 LPA
5+ years: ₹20–35 LPA
Salaries grow faster if you gain certifications and work on real ML projects.
Yes, MLOps is considered a premium tech career. Since very few engineers specialize in this field, companies are willing to pay high salaries to attract and retain skilled talent.
Top-paying industries include finance, healthcare, e-commerce, SaaS, IT consulting, and big tech companies. These industries rely heavily on AI/ML solutions, so they pay higher salaries for MLOps skills.
Yes, many companies, especially global ones, offer remote MLOps jobs. Remote roles often pay in dollars, which can be much higher than average Indian salaries.
Yes, certifications like AWS Certified Machine Learning, Kubernetes (CKA), or TensorFlow Developer can increase your value. Certified professionals often earn 15–20% more than non-certified ones.
At Google India, MLOps Engineers can earn ₹25–40 LPA, depending on skills and experience. Senior-level engineers with global exposure can earn even more.
Amazon India offers ₹20–35 LPA for experienced MLOps Engineers. For freshers, salaries start around ₹10–12 LPA.
At Microsoft, MLOps salaries range from ₹18–30 LPA for mid-level engineers, while senior-level professionals can cross ₹35 LPA.
Abroad, especially in the US, MLOps Engineers earn $120,000–$160,000 per year (₹1 Cr+ per annum). This is much higher compared to India, though the cost of living is also higher.
Yes, freelancing is possible. Many global companies hire freelance MLOps engineers to set up pipelines, deploy ML models, and monitor AI systems. Freelancers can charge $30–100 per hour.
After 5 years, if you specialize in MLOps tools and cloud platforms, you can expect ₹20–28 LPA in India. In leading tech hubs, salaries may go beyond ₹30 LPA.
Yes, AI-driven startups in India pay well to attract top talent. Salaries may be ₹12–25 LPA, and many startups also offer equity or stock options, which can increase your total compensation.
MLOps generally pays more because it requires both DevOps knowledge and machine learning expertise. DevOps engineers earn around ₹8–15 LPA, while MLOps engineers earn ₹12–25 LPA on average.
Yes, experienced MLOps Engineers often move into AI/ML Architect or ML Engineering Manager roles, which pay ₹35–50 LPA in India.
In Bangalore, salaries are 15–25% higher than the national average. Entry-level starts at ₹8–12 LPA, mid-level at ₹15–25 LPA, and senior-level can go beyond ₹35 LPA.
Yes, Python is a must-have for writing automation scripts, ML pipelines, and integrations. Without Python, it’s hard to get into MLOps, and salaries are much lower.
Yes, finance and fintech companies rely heavily on ML models for fraud detection and risk management. They often pay 20–30% more than average IT companies.
The demand for MLOps is growing fast. Experts predict salaries will rise 20–30% in the next 3–5 years as more companies adopt AI at scale.
MLOps is more specialized because it combines DevOps, cloud, and machine learning. This is why fewer people master it, and salaries are higher compared to other tech roles.
Government projects in defense, healthcare, and smart cities are starting to hire MLOps experts. However, salaries are usually lower than private sector but offer job security.
To increase your salary, focus on
- Learning advanced cloud skills (AWS/GCP/Azure)
- Mastering CI/CD and Kubernetes
- Getting certifications
- Gaining hands-on experience in ML projects
With these, you can boost your pay by 30–50% within 2–3 years.