Dedicated AI Engineers
Hire AI and ML Engineers
AI engineers who ship features with measurable accuracy targets.
A demo that works on five examples is not a product. We place engineers who build the evaluation harness before they tune the prompt.
Quick Answer
Updated August 22, 2026
What does it cost to hire an AI or ML engineer?
A senior AI or ML engineer from India costs $42–$60 per hour, roughly ₹4.8–6.8 lakh per month on a dedicated engagement, and a lead or architect $60–$85 per hour. The premium over standard engineering is real and reflects genuine scarcity: many engineers can call a model API, far fewer can build an evaluation harness, control token spend, and tell you honestly when the accuracy is not good enough to ship.
Senior hourly rate
$42 – $60
Time to start
10 – 15 working days
First deliverable
An evaluation baseline
Best for
- Products adding LLM features that must work on real user input
- Teams with a working demo that will not survive production
- Companies whose AI feature costs more per call than it earns
Not best for
- Exploratory research with no product target
- Teams expecting a model to replace a missing data strategy
- Projects where nobody will define what "correct" means
Rate Card
What does it cost to hire ai & ml engineers?
AI seats price above standard engineering because the supply of engineers who can evaluate and cost-control an LLM system, rather than just call one, is genuinely thin.
| Level | Experience | Monthly (INR) | Hourly (USD) | Best for |
|---|---|---|---|---|
| Mid-level | 3–5 years | ₹3,00,000 – ₹4,20,000 | $28 – $40 | Prompt and pipeline work inside a defined product surface |
| Senior | 5–8 years | ₹4,80,000 – ₹6,80,000 | $42 – $60 | Retrieval design, evaluation harnesses, cost and latency control |
| Lead / architect | 8+ years | ₹6,50,000 – ₹9,00,000 | $60 – $85 | End-to-end AI system design with real accuracy and spend targets |
Rates are for dedicated engagements with at least four hours of daily overlap with your working hours. Longer commitments reduce the rate; more overlap increases it.
Capability
What our ai & ml engineers actually do
- Retrieval pipelines — chunking, embedding, hybrid search, and reranking
- Evaluation harnesses with labelled sets, so quality is a number and not a feeling
- Prompt and context engineering against a measured baseline rather than by intuition
- Agent and tool-use design, including failure handling and cost ceilings
- Latency and spend control — caching, model routing, batching, streaming
- Guardrails, PII handling, and the honest limits of what a model should decide
Working stack
We staff for the stack you already run wherever we can. Where we think a different choice would serve you better, we will say so once — with the reasoning — and then work in yours.
Common Engagements
What teams usually hire AI and machine learning engineers for
Retrieval over your own documents
A RAG pipeline where retrieval quality is measured, not assumed, and answers cite the source they came from.
Production support agents
Customer-facing assistants with escalation paths, cost ceilings, and a clear boundary on what they may decide alone.
Document and data extraction
Turning unstructured documents into validated structured records with a confidence signal and a human review path.
Rescuing an AI demo
Taking a prototype that impressed in a meeting and finding out — with numbers — what it actually does on real inputs.
Vetting
How we decide who is good enough to place
Vetting is the entire value of hiring through an agency rather than a marketplace. If we are not rejecting most applicants, we are not doing the job you are paying for.
Step 1
Technical interview
A senior engineer from our team runs the interview. Not a recruiter with a checklist, and not an automated coding test.
Step 2
Practical exercise
A task close to the work you are actually hiring for, reviewed the way we would review a pull request.
Step 3
Reference and delivery check
We ask what they shipped recently, who reviewed it, and what went wrong on it. The last question is the useful one.
Step 4
Fit call with you
You speak to the engineer before committing. If the fit is wrong at that point, nothing has been signed.
Comparison
An AI engineer vs a backend developer using a model API
Calling a model is easy. Knowing whether the output is good enough to put in front of a customer is the actual job.
With SoftwareCrafting
- Evaluation set and baseline built before any prompt tuning
- Cost per request treated as a design constraint from day one
- Retrieval quality measured separately from generation quality
- A written recommendation when the honest answer is not to ship it yet
Typical alternative
- Quality judged by trying a handful of examples by hand
- Token spend discovered on the first monthly invoice
- Bad answers addressed by making the prompt longer
- Every result presented as a success because the demo worked
Engagement Models
Four ways to work with us, and when each one fits
Dedicated seat
One engineer working full time inside your team, your repo, and your process. Monthly rolling, two weeks notice.
Ongoing roadmap work with no defined end date
Part-time seat
From roughly 20 hours a week. Below that, context reloading eats most of the value, and we will say so.
Specialist input alongside an existing team
Fixed-scope project
A defined deliverable at an agreed price. Only where scope is genuinely clear enough to fix without a change-request cycle.
Migrations, audits, and well-specified builds
Delivery pod
Three to four people including QA and a named delivery owner accountable for the sprint outcome.
Whole product areas you want owned end to end
What you get either way
- You interview the engineer before anything is signed
- Replacement at our cost if the fit is wrong in the first two weeks
- IP assignment and NDA signed before technical access is granted
- Code committed to your repository, under your organisation, from day one
- Monthly rolling contract — two weeks notice, no annual lock-in
Quick Brief
Start the conversation here
Tell us the role, the stack, and when you need someone. We will come back with availability and a rate.
Your Name
Work Email
What do you need help with?
Proof
Products we have shipped with AI and machine learning
FAQ
Questions buyers ask before hiring AI and machine learning engineers
How much does it cost to hire an AI engineer in India?
Mid-level AI engineers cost $28–$40 per hour, seniors $42–$60, and leads $60–$85, which is roughly ₹3–4.2 lakh, ₹4.8–6.8 lakh, and ₹6.5–9 lakh per month respectively. That is a clear premium over standard engineering and it reflects real scarcity.
Which models do you build on?
Mostly the Claude and Gemini families, chosen per workload on cost, latency, and quality rather than loyalty. We design the integration so switching models later is a configuration change, not a rewrite.
How do you measure whether an AI feature is good enough?
With a labelled evaluation set built from your real data before any tuning starts. That gives a baseline number, and every change after it either moves the number or gets reverted. Without it, prompt work is guesswork with a confident tone.
How do you control AI running costs?
Model routing so cheap requests do not hit expensive models, prompt caching, retrieval that sends less context, and a hard per-request ceiling. We report projected monthly spend at realistic volume before the feature ships, not after.
Do we need our own model or is an API enough?
For almost every product we see, an API is enough. Fine-tuning and self-hosting are worth considering at high, stable volume or under a data-residency requirement. We will say when you are genuinely in that territory, which is rarer than the market implies.
Can you take over an existing AI prototype?
Yes, and it is a common engagement. The first deliverable is usually an honest evaluation of what the prototype actually achieves on real inputs, which is frequently the most valuable part of the project.
Related services
The service pages behind this role
What this engineer works on day to day, and how we scope it commercially.