MLOps Engineer Salary in India (2026): By Experience, City & Company
MLOps is one of the fastest-growing AI hiring categories in India — reported as growing quickly — because most companies now have more models than they have people who can reliably deploy and monitor them. Pay tracks close to AI/ML Engineer at the same level, with a real premium for Kubernetes and cloud-platform depth specifically.
Key insight: Bengaluru pays the highest average for this role, followed by Hyderabad, Pune and Mumbai — and the premium is real but not enormous (Glassdoor, 2026, puts Bengaluru's average at ₹17.25L against a ₹16L national median). The bigger lever is company type: specialists at product companies and GCCs report ₹40–55L+, well above the ₹20–35L senior band typical elsewhere.
Key figures at a glance
- Entry (0–2 yr)
- ₹6L – ₹10L (₹8L typical)
- Mid-level (2–5 yr)
- ₹8.3L – ₹22L (₹16.2L typical)
- Senior (5+ yr)
- ₹20L – ₹35L (₹27L typical)
- Specialist / Staff
- ₹35L – ₹55L (₹45L typical)
Salary ranges on Pathvio are estimates compiled from salary information published across multiple job portals. They show the CTC range you can typically expect for a role, based on your years of experience, job role and skills. They are not an exact or fixed figure, and not a promise of what any employer will offer: actual pay depends on the company, the location, your interviews and how the offer is negotiated.
Indicative estimates compiled from external salary information · Annual CTC in INR · About these figures
MLOps Engineer Salary by Experience in India
All figures are annual CTC in Indian Rupees, shown as indicative ranges (low end, typical, high end, ceiling) — not survey percentiles.
Product-based startups in Bengaluru/Hyderabad pay toward the top of this band; service companies and smaller cities pay closer to ₹6–7L.
National Glassdoor spread (2026) — the widest band in this table, reflecting how much company type moves the number at this stage.
Senior MLOps engineers at product companies command real scarcity pay — reliable production ML operations is still a rare combined skill set.
Specialists at top product companies and GCCs cross this range; deep Kubernetes + multi-cloud + MLOps-maturity-ladder experience (see the career guide) is what gets you here.
| Experience Band | Low end | Typical ↑ | High end | Ceiling |
|---|---|---|---|---|
| Entry (0–2 yr) | ₹6L | ₹8L | ₹10L | ₹12L |
| Mid-level (2–5 yr) | ₹8.3L | ₹16.2L | ₹22L | ₹32.1L |
| Senior (5+ yr) | ₹20L | ₹27L | ₹35L | ₹42L |
| Specialist / Staff | ₹35L | ₹45L | ₹55L | ₹65L |
Asked for your expected CTC, or holding an offer near these numbers? How to negotiate salary in India
MLOps Engineer Salary by City
Estimated difference vs India-wide, by city. These are indicative ranges compiled from salary information published across job portals, not exact offers.
Glassdoor (2026): a ₹17.25L average against a ₹16L national median, with a P25–P75 spread of ₹10.75L–₹22L.
Reported as the second-highest-paying city for this role — Microsoft, Amazon and a growing GCC base drive demand.
Third-highest reported average; strong manufacturing and BFSI presence adopting MLOps practice.
BFSI-heavy demand, but reported as paying closer to the national baseline than the other three metros above.
Real demand from ed-tech and consulting-adjacent GCCs, but a thinner concentration of dedicated MLOps roles than the southern tech hubs.
Growing but still a smaller market for this specific specialisation relative to Bangalore/Hyderabad.
MLOps Engineer Salary by Company Type
Company type is a major salary variable in India — often as important as years of experience.
MLOps-labelled roles here are often closer to general DevOps with occasional ML pipeline work, not a dedicated specialisation.
Real, dedicated MLOps ownership over a smaller number of production models.
Larger model fleets, more automation maturity expected, ESOP upside at senior levels.
The top of the market for this role — genuinely mature MLOps practice (see the maturity-levels framework in the career guide) and the deepest specialisation demand.
Skills That Boost Your MLOps Engineer Salary
Indicative skill premiums. Actual pay depends on role, employer and experience.
One of the most consistently cited differentiators between a DevOps generalist and a genuinely hireable MLOps specialist.
Companies running hybrid or multi-cloud ML infrastructure pay a premium for engineers who aren't locked to one platform's tooling.
Model versioning discipline is exactly the gap most Level-0 companies (see the career guide's maturity framework) are trying to close first.
The specific, ML-native skill that generic infrastructure monitoring doesn't cover — genuinely scarce.
The newest, fastest-growing sub-specialisation as generative AI deployment scales — see the career guide for what it adds on top of standard MLOps.
MLOps Engineer Career Path in India
MLOps careers progress by ownership scope — from operating individual models to owning the reliability of an entire ML platform.
Move from operating models someone else built to owning the full pipeline — training automation, deployment, monitoring — for a real production system.
Own the ML platform across multiple teams' models, not just one; this is where moving a company up the MLOps maturity ladder (see the career guide) becomes your actual job.
Architecture ownership across the company's entire ML infrastructure, plus the organisational influence to set standards other teams follow.
MLOps Engineer Interview Process in India
MLOps interviews blend standard DevOps/infrastructure rounds with ML-specific scenario questions.
Containerisation, orchestration, CI/CD design — the same bar as a strong DevOps interview.
Prep tip: Don't undersell general infra skill just because the role has 'ML' in the title — it's still tested rigorously.
How would you detect a model has silently degraded in production? How would you design a safe rollback for a bad model version?
Prep tip: Reference concrete monitoring signals (prediction distribution shift, not just error rates) — this is exactly what separates a real MLOps answer from a generic DevOps one.
Design the pipeline from data to trained model to monitored deployment for a given scenario.
Prep tip: State your assumptions about scale and automation maturity level explicitly before diving into the design.
Free Tools Before You Apply
From the Pathvio Blog
MLOps Engineer Career Guide
Docker, Kubernetes, MLflow — the real tools, and why hiring is growing 60–80% year on year.
Read articleAI/ML Engineer Career Guide
The natural sibling role if your interest is closer to model architecture than infrastructure.
Read articleTop AI Roles in India, Compared
Where MLOps fits among the seven AI roles compared — pay, eligibility, growth.
Read articleSalary Negotiation in India
Exact scripts for the expected-CTC question and the offer call, and what to negotiate when fixed pay won't move.
Read articleFind out exactly where you stand
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