Bengaluru, Karnataka
Hire machine learning engineers in Bengaluru
Key facts
Shortlist
48 hours
Typical time to hire in Bengaluru
21–40 days
Talent availability
Scarce — plan early
Salary bands
Machine learning engineers salaries in Bengaluru
Junior
1–3 yrs
Mid
3–6 yrs
Senior
6–10 yrs
Lead
10+ yrs
Show as table
| Level | Bengaluru |
|---|---|
| Junior (1–3 yrs) | ₹11–22 LPA |
| Mid (3–6 yrs) | ₹22–41 LPA |
| Senior (6–10 yrs) | ₹39–70 LPA |
| Lead (10+ yrs) | ₹65–103 LPA |
Indicative ranges compiled by Emplyify from market data, Q4 2026. They are a starting point for budgeting, not an offer benchmark; our Talent Index will replace them with pipeline data.
Notice periods in Bengaluru
Product-company engineers often have 30–60 days' notice; large GCCs and service companies 60–90 days.
Local hiring notes
- Competition for senior AI/ML and platform engineers is intense; speed of process often decides outcomes.
- Candidates compare equity and remote flexibility closely; be ready to explain both.
- Commute between north and south Bengaluru affects acceptance; note office location in the brief.
Where the talent works
Outer Ring Road · Whitefield · Electronic City · Manyata and Hebbal
GCC hiring in Bengaluru →Our screen
Skills we vet for machine learning engineers
Good ML engineers are rare because the job is two jobs: modelling and production engineering. We screen for both — evidence of shipped models, not just Kaggle ranks.
| Skill | How we evidence it |
|---|---|
| Python and ML frameworks | Walk-through of a model they trained and deployed, including data and evaluation choices |
| LLM application engineering | Retrieval, evaluation and cost trade-offs on a real project |
| MLOps | How they versioned data and models, monitored drift and rolled back |
| Evaluation discipline | Offline vs online metrics, and a time a metric misled them |
| Software engineering | Code review of a past PR or a short live exercise |
Sample interview loop
A loop that respects candidates' time
1. Recruiter screen · 30 min
Motivation, notice period, CTC, must-have skills
2. Technical deep-dive · 60 min
Past systems, trade-offs, depth on core skills
3. Practical exercise · 60–90 min
Live problem or take-home review, never unpaid multi-day work
4. Hiring manager · 45 min
Team fit, ownership, communication
Screening questions we ask
- “Describe a model you shipped. How did you know it was working in production?”
- “When did you choose not to use an LLM, and why?”
- “How did you build an evaluation set for a retrieval or generation system?”
- “What broke after deployment, and how did you find out?”
Questions
Can you find engineers with production LLM experience?
Yes, but the pool is small and moves fast. We screen for retrieval, evaluation and cost control on real projects, not prompt demos, and we say upfront if your budget is below market.
Do you test candidates on ML theory?
Only as far as the role needs it. Our screen focuses on production decisions; your team can go deeper on theory in the technical round.
Is Bengaluru too competitive for a new GCC?
It is the most competitive market, but also the only one with the senior depth some teams need. A clear employer story and a fast interview loop matter more here than anywhere else.
Two-minute brief
Hiring machine learning engineers in Bengaluru?
We've pre-filled the role. A recruiter replies within 4 business hours; your shortlist follows within 48 hours.