Full disclosure: Ailoitte provides offshore AI engineering teams. The market data below is real regardless.
What Does a Senior ML Engineer Actually Cost in 2025?
| Location | Total Comp |
|---|---|
| US | $250K–$350K |
| India (pre-vetted, production experience) | $38K–$80K |
US hiring means competing with FAANG for the same candidates. 1.5 million US software engineering positions are projected to go unfilled through 2028 — and the ML layer is the worst of it.
That $38K–$80K offshore figure isn't for generalists. It's for engineers with real RAG pipeline, agent infrastructure, and ML pipeline work in their history.
That's a 70% cost difference. Not a rounding error.
Is Offshore AI Engineering Actually as Good?
With proper vetting: yes.
Without it: frequently no.
The failure mode is optimising for speed of placement over depth of screen. Here's what good vetting actually looks like:
- Has this engineer shipped the specific thing you need — RAG, agents, evaluation infrastructure — in production?
- Can they describe a real failure and what they changed?
- Is there genuine overlap availability for collaboration?
Engineers who have shipped production AI systems get specific about failures. Engineers who haven't get vague. That distinction is the entire quality filter.
Why Is This Pricing Gap Closing?
Three forces are compressing it simultaneously:
1. FAANG scaling India
Google, Microsoft, Meta — all significantly expanded India engineering in the past 18 months. They recruit from the same pool, and their presence pushes offshore AI engineer salaries up.
2. India's domestic market
Indian unicorns now pay at levels that only US-remote roles matched two years ago. Internal demand is real and growing.
3. Remote work parity
Engineers compare US-remote and offshore-remote directly now. The geographic premium collapses when geography stops mattering.
Estimate: ~18 months for the current spread to close meaningfully. And that's conservative.
How Long Does an Offshore AI Engineer Take to Ramp?
Two weeks — with structured onboarding.
That means:
- Clear documentation
- Defined first sprint
- 3–4 hours of daily overlap
- Senior architect available for questions
The 6–8 week ramp stories you hear are almost always disorganised onboarding, not an offshore quality problem.
What Does This Mean Practically?
If you're at Series A–B trying to hire ML engineers:
| Factor | Reality |
|---|---|
| US market | Genuinely difficult without compelling equity at $280K+ |
| Offshore quality | Equivalent with proper vetting |
| Window for current pricing | ~18 months |
| Ramp time | 2 weeks with structured onboarding |
Every month of failed US hiring is a month your product isn't shipping.
The Bottom Line
Ailoitte builds AI engineering teams around this model. Full market analysis with sourcing and retention data on Medium →
How long have you been trying to fill an AI engineering role? What has the actual pipeline looked like? Drop it in the comments.
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