Hire MLOps Engineers in India: Cost, Steps & Mistakes

Looking to hire MLOps engineers in India but not sure where to start? This guide is for startup founders, CTOs, and engineering leaders tired of overpaying for MLOps talent, dealing with compliance headaches, and struggling to find engineers who can actually ship ML models to production.

We've helped foreign companies build engineering teams in India, so we'll walk you through the real challenges, what skills to look for, how to hire step by step, and how to cut costs without compromising on quality.

Let's make your next MLOps hire faster, smarter, and fully compliant!

Why Hire MLOps Engineers in India?[toc=Why Hire MLOps Enginners]

Finding skilled MLOps talent in the US or UK is expensive and competitive. Senior MLOps engineers in the US earn $120,000 to $160,000+ per year, and recruiters report compensation has jumped roughly 20% year-over-year.

From what we've seen helping 300+ global companies build teams in India, the country solves both the cost and availability problem.

Here's why:

1. A massive and growing AI talent pool

• India ranks #1 globally in AI skill penetration (NASSCOM) and holds about 16% of the world's AI talent.
• 1.25 million AI specialists projected by 2027, with 1.5 million engineering graduates produced every year.
• Top institutions (IITs, NITs, IIITs) feed a strong pipeline of engineers skilled in machine learning, data engineering, and software engineering.

2. 60 to 80% cost savings, same skill sets

• Mid-level MLOps engineers in India: $14,000 to $22,000/year. Senior-level: $24,000 to $42,000/year.
• Compare that to $120,000+ in the US for similar experience with the same cloud platforms (AWS, GCP, Azure), ML frameworks (TensorFlow, PyTorch, MLflow), and CI/CD tools.

3. Deep MLOps ecosystem in key tech hubs

• Bangalore, Hyderabad, Pune, and Chennai are major MLOps hiring centers.
• India hosts 1,700 to 1,800 global capability centers (GCCs), many running production ML systems at scale.
• A growing pool of dedicated MLOps engineers with hands-on experience in ML pipelines, model monitoring, and scalable infrastructure.

4. Time zone and remote-readiness

• IST (UTC+5:30) creates natural overlap with US mornings and UK afternoons.
• Most Indian engineers already work with GitHub, Slack, and async workflows, making them a natural fit for remote-first SaaS teams.

5. Government-backed AI investment

• India's National AI Mission and IndiaAI platform are actively funding AI education, reskilling programs, and industry-academic partnerships, building a steady pipeline of production-ready AI engineers and data scientists.

For startups shipping AI systems under budget pressure, hiring MLOps engineers in India gives you access to skilled talent who can manage your entire machine learning process, from data processing to model deployment to continuous monitoring, at a fraction of Western costs.

What Skills Should You Look for in MLOps Engineers?[toc=Essentail Skills]

Hiring an MLOps engineer is not the same as hiring a data scientist or a traditional software engineer. MLOps sits at the intersection of machine learning, DevOps, and data engineering.

You need someone who can take ML models from a notebook to production and keep them running reliably at scale.

Here's what to prioritize:

Core ML and data fundamentals

• A solid understanding of machine learning algorithms, model training, evaluation, and how machine learning models behave in production environments. They don't need to build models from scratch, but they need to understand what they're deploying.
• Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with data processing tools (Apache Spark, Hadoop) and data structures that power ML pipelines.

CI/CD and pipeline automation

• This is the backbone of MLOps. Your hire should be able to build and manage CI/CD pipelines specifically for ML workflows, not just traditional software. That means automating data preprocessing, model training, evaluation, model versioning, and deployment.
• Look for experience with tools like Jenkins, GitHub Actions, GitLab CI, Airflow, Kubeflow, or MLflow.

Cloud platforms and infrastructure management

• Almost every production ML system runs on cloud. Your MLOps engineer should have hands-on experience with at least one of AWS, GCP, or Azure, including services like SageMaker, Vertex AI, or Azure ML.
• They should be comfortable with containerization (Docker), orchestration (Kubernetes), and managing scalable infrastructure for ML workloads.

Model monitoring and observability

• Deploying a model is only half the job. MLOps engineers ensure models keep performing after deployment by setting up continuous monitoring, detecting data drift and model drift, tracking performance metrics, and triggering model retraining when needed.
• Look for experience with tools like Prometheus, Grafana, Weights & Biases, or Evidently AI.

Version control and reproducibility

• Beyond standard Git, MLOps requires versioning for data, models, and experiments. Tools like DVC (Data Version Control), MLflow, or Neptune.ai are common in this space.
• The goal: anyone on the team should be able to reproduce a model training run and trace back to the exact data and code that produced it.

Software engineering fundamentals

• Strong Python skills are non-negotiable. Beyond that, look for knowledge of data structures, APIs (REST/gRPC), testing practices, and clean code principles.
• MLOps engineers work at the intersection of multiple teams (data scientists, AI engineers, platform teams), so they need to write production-grade code, not just scripts.

Nice-to-haves that signal depth

• Experience with feature stores (Feast, Tecton).
• Knowledge of data governance and compliance frameworks.
• Familiarity with real-time monitoring and load balancing for inference workloads.
• Domain expertise in your specific industry (fintech, healthtech, SaaS) is a bonus, but strong MLOps fundamentals matter more.

One thing worth noting from our experience: the best MLOps hires are not the ones who check every single box. They're the ones with strong software engineering instincts who can learn new MLOps tools quickly. Prioritize problem-solvers over checkbox candidates.

How to Hire MLOps Engineers in India (Step-by-Step)[toc=Hiring Steps]

Hiring MLOps engineers in India is not the same as posting a job on LinkedIn and waiting. The talent is in high demand, the role is niche, and the best candidates get snapped up fast. Here's a step-by-step process that actually works:

Step 1: Define the role clearly

Before you start sourcing, get specific about what you need. "MLOps engineer" means different things at different companies. Ask yourself:

• Do you need someone to build ML pipelines from scratch, or maintain existing ones?
• Are they managing cloud architecture (AWS, GCP, Azure) or working within a platform like SageMaker or Vertex AI?
• Will they own model monitoring, CI/CD pipelines, or the entire ML lifecycle end to end?
• What ML frameworks does your team use (TensorFlow, PyTorch, etc.)?

The clearer your job description, the faster you filter out mismatches. Vague postings attract vague candidates.

Step 2: Source from the right channels

Traditional hiring platforms work, but for MLOps talent in India, you want to go deeper:

• LinkedIn and Naukri are the obvious starting points. Filter for engineers with experience in MLOps tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes.
• GitHub and Kaggle profiles tell you more than resumes. Look for candidates who have built real ML pipelines or contributed to open-source MLOps projects.
• AI/ML communities like MLOps Community (Slack), Weights & Biases forums, and local meetups in Bangalore, Hyderabad, and Pune are where serious practitioners hang out.
• Referrals from your existing India team (if you have one) are often the fastest path to quality hires.

Step 3: Screen for production experience, not just theory

This is where most companies get it wrong. A lot of candidates know ML concepts but have never shipped a model to production.

In your screening, prioritize:

• Have they built and maintained CI/CD pipelines for ML models (not just software)?
• Can they describe how they handled data drift or model drift in a live environment?
• Do they have hands-on experience with containerization (Docker) and orchestration (Kubernetes)?
• Have they worked with real-time monitoring and model retraining in production environments?

A short take-home assignment simulating a real MLOps scenario (deploy a model, set up monitoring, handle a drift alert) will tell you more than a whiteboard interview.

Step 4: Assess cultural and communication fit

If you are a remote-first SaaS company in the US or UK, communication matters as much as technical skills. During interviews, evaluate:

• Can they explain technical decisions clearly to non-technical stakeholders?
• Are they comfortable with async communication (Slack, Loom, written updates)?
• Do they proactively flag issues or wait to be asked?

From what we've seen working with global clients, the best India-based MLOps engineers are not just technically strong but also great communicators who thrive in distributed teams.

Step 5: Decide on your hiring model

This is where it gets practical. You have a few options:

• Direct hiring (set up an entity in India): Full control, but slow and expensive. Entity setup takes months and comes with compliance overhead around Indian labor laws, EPF, gratuity, and tax filings.
• Freelance/contractor hiring: Fast to start, but risky long-term. Contractors in India can trigger permanent establishment risks, and you have limited control over retention and IP.
• Employer of Record (EOR): You hire the talent, and the EOR like Wisemonk handles all legal employment, payroll, benefits, and compliance in India on your behalf. No entity needed. This is the fastest path for startups and scaling companies who want to hire dedicated MLOps engineers without the legal headaches.

Step 6: Onboard with intention

Once you've made the hire, don't just drop them into Slack and hope for the best.

A structured onboarding that includes access to your ML infrastructure, clear documentation on existing pipelines, intro calls with data scientists and AI engineers on your team, and defined 30/60/90-day goals makes a huge difference in ramp-up speed and retention.

How Much Does It Cost to Hire an MLOps Engineer?[toc=Cost to Hire]

This is usually the first question founders and CTOs ask, and the answer depends heavily on where you hire and at what experience level.

Let's break it down with real numbers:

MLOps engineer salary in the US (2026)

• Average: $130,000 to $165,000/year (Glassdoor, Salary.com, February 2026).
• Senior MLOps engineers: $200,000+ per year. Top earners at companies like Google and NVIDIA can push past $240,000.
• That's just base salary. Add benefits, equity, recruiting fees, and overhead, and your total cost per hire easily crosses $200,000 to $250,000 annually.

MLOps engineer salary in India (2026)

• Entry-level (0 to 2 years): $7,000 to $12,000/year (6 to 10 LPA).
• Mid-level (2 to 5 years): $14,000 to $22,000/year (12 to 18 LPA).
• Senior-level (5+ years): $24,000 to $42,000/year (20 to 35 LPA).
• Top-tier senior or architect-level roles at product companies or with global exposure can go up to $50,000 to $72,000/year (40 to 60 LPA).

The average MLOps engineer salary in India sits around $18,500/year (roughly 15.5 LPA), according to Glassdoor India data as of February 2026.

Check out our CTC to In-Hand Salary Calculator.

What does this mean for your budget?

For a Series A startup spending $150,000 to $200,000 on a single MLOps engineer in the US, that same budget can get you a dedicated team of 3 to 4 mid-to-senior MLOps engineers in India. That's not a hypothetical. That's what we see our clients do regularly.

But salary is not your only cost. Here's what else to factor in:

• Recruiting costs: Traditional hiring through agencies can run 15 to 25% of annual salary per hire. In a competitive market like MLOps, it can take months to close a candidate.
• Benefits and compliance: If you hire directly in India, you need to handle EPF (provident fund), gratuity, professional tax, health insurance, and Indian labor law compliance. Getting any of this wrong can lead to legal and financial headaches.
• Infrastructure and tooling: Your MLOps engineers will need access to cloud resources (AWS, GCP, Azure), MLOps tools (MLflow, Kubeflow, Airflow), and monitoring platforms. Budget for this regardless of location.
• Entity setup (if going direct): Setting up a legal entity in India takes time and costs $15,000 to $25,000+ upfront, plus ongoing accounting and legal fees.

The EOR advantage

This is where an Employer of Record (EOR) changes the math. With Wisemonk EOR, you skip entity setup entirely. We handle payroll, taxes, benefits, and compliance in India, so you just focus on hiring the right MLOps talent.

Your total cost per engineer through an EOR is a fraction of what you'd spend hiring in the US, and you're fully compliant from day one. (Starting at $99 per employee/month)

What Mistakes Should You Avoid When Hiring MLOps Engineers?[toc=Mistakes to Avoid]

According to a Gartner survey, nearly 85% of AI projects fail to reach production. A big reason? Companies get the hiring wrong.

Here are the most common mistakes we see global companies make when trying to hire MLOps engineers in India, and how to avoid them:

• Confusing MLOps with data science or DevOps: MLOps engineers don't build models. They take models to production and keep them running. If your job description mixes these roles, you'll attract the wrong candidates.
• Hiring for tool checklists, not production experience: A resume listing Docker, Kubernetes, and MLflow means nothing if the candidate has never handled a live pipeline failure or model drift. Ask about real production incidents in interviews.
• Ignoring India compliance: Hiring MLOps engineers as contractors to "move fast" can expose you to permanent establishment risk, misclassification penalties, and tax issues. Use an EOR or set up a legal entity to stay compliant.
• Skipping onboarding: Giving someone Slack access and hoping for the best doesn't work. Without access to your ML infrastructure, pipeline documentation, and clear 30/60/90-day goals, even great hires will struggle to ramp up.
• Optimizing only for cost: Cheapest hire ≠ best hire. Underpaying relative to the India market leads to poor retention and rework. Pay competitively (see salary ranges in Section 4) to attract dedicated MLOps engineers who deliver.
• Not testing communication skills: For remote, cross-timezone teams, written communication matters as much as technical ability. Test for async readiness (Slack updates, documentation, proactive flagging) during the interview, not after.
• Expecting one person to do everything: Don't look for a unicorn who handles data engineering, model development, and ML infrastructure. Hire for deep MLOps expertise and pair them with your existing data scientists and AI engineers.

Get Started With Wisemonk EOR[toc=Choose Wisemonk EOR]

You've done the research. You know India has the MLOps talent, the cost advantage, and the engineering depth to scale your AI team. Now you need a way to actually hire them without spending months on entity setup, compliance paperwork, or legal risk.

That's exactly what Wisemonk does.

We help global companies hire MLOps engineers in India, fast and fully compliant. No entity required. No permanent establishment risk. No guessing on Indian labor laws.

Here's what you get with Wisemonk EOR:

• Hire in days, not months: We handle employment contracts, onboarding, payroll, and benefits so you can focus on finding the right MLOps talent for your AI team.
• Full India compliance, handled for you: EPF, gratuity, professional tax, health insurance, tax filings. We manage all of it so you don't have to.
• Payroll for your entire India team: We already manage $20M+ in payroll for 2,000+ employees across India. Your MLOps engineers get paid accurately and on time, every month.
• Benefits that help you retain top talent: Competitive health insurance, equity management, and locally relevant perks that keep your dedicated MLOps engineers from jumping ship.
• One point of contact: No bouncing between legal, HR, and finance vendors. Wisemonk is your single partner for everything related to employing talent in India.

300+ global companies already trust us to build and manage their India teams. Whether you're hiring your first MLOps engineer or scaling an entire AI team, we've done this before and we'll make it seamless for you.

Talk to us today and hire your first MLOps engineer in India this week!