Hire Generative AI Engineers Who Ship LLM Products
When you hire generative AI engineers through Aizecs, you get people who have already put LLM systems in front of paying users — RAG pipelines over messy internal data, multi-step agents, prompt pipelines with evals and guardrails. Not notebook demos. You interview the actual named engineer, and profiles land in your inbox within 48 hours.
48h
Named profiles delivered
<1 in 20
Applicants pass our vetting
$28/hr
Generative AI rates start at
4+ hrs
Overlap with US Eastern
Transparent Pricing
What It Costs to Hire Generative AI Engineers
Generative AI talent commands a 20–30% premium over generalist engineers everywhere — but 'premium' shouldn't mean Toptal's $90–$180+/hr. Our rates are published, per seniority, before you ever get on a call.
| Role / Seniority | Aizecs (India) | Toptal / Turing | Upwork Freelancers | US In-House |
|---|---|---|---|---|
| Mid-Level Generative AI Engineer (3–5 yrs) | $28–$50/hr | $90–$130/hr | $40–$70/hr | $150–$190/hr |
| Senior Generative AI Engineer (5–8 yrs) | $45–$80/hr | $110–$160/hr | $50–$90/hr | $180–$230/hr |
| Staff / Lead LLM Engineer (8+ yrs) | $55–$85/hr | $130–$180+/hr | $60–$100/hr | $200–$250/hr |
| MLOps / LLMOps Specialist | $45–$80/hr | $100–$160/hr | $40–$90/hr | $160–$220/hr |
Hourly bill rates for dedicated, full-time engineers (2025–26 benchmarks). Upwork ranges are freelance asking rates with no independent vetting. US in-house figures reflect fully loaded cost — salary, equity, benefits, and overhead.
How It Works
From Intro Call to Working RAG Pipeline in Two Weeks
Hiring for an emerging role shouldn't take a quarter. Here's the path most clients follow from first conversation to an engineer shipping in their repo.
Map the LLM problem
A 30-minute call to understand what you're building — RAG over documents, an agentic workflow, a copilot — plus your stack, model providers, and timezone needs.
Review named candidates in 48 hours
We send 2–4 profiles of engineers who have shipped comparable systems, with links to real work. Run your own technical rounds — prompt-pipeline design, eval strategy, whatever you'd ask an in-house hire.
Trial week on real tickets
Your chosen engineer spends a paid week inside your repo and sprint board, working actual backlog items. Not convinced? Pay for that week only and we swap in a replacement free.
Continue month-to-month
No annual lock-in. Extend, add a second generative AI engineer, or wind down with 30 days' notice as your roadmap evolves.
Roles We Staff
Specializations Across the Generative AI Stack
Generative AI is a family of roles, not one job title. Whether you searched for prompt engineers, LLM engineers, or agentic AI engineers, these are the specializations we place.
LLM Application Engineers
Build product features on Claude and GPT APIs — structured outputs, function calling, streaming UX — with the latency budgets and retry logic production demands.
RAG Pipeline Engineers
Design retrieval systems end-to-end: chunking strategy, embeddings, pgvector or Pinecone, hybrid search, and reranking tuned against real recall metrics.
Agentic AI Engineers
Ship multi-step agents with LangGraph and similar frameworks — tool use, state management, human-in-the-loop checkpoints, and failure recovery that survives production.
Prompt Engineers
Treat prompts as versioned, tested artifacts — systematic iteration backed by eval suites rather than vibes, integrated into CI so regressions get caught before users do.
Evals & Guardrails Engineers
Stand up LLM observability, automated eval harnesses, hallucination detection, and safety filters so you can change models and prompts with confidence.
Fine-Tuning Engineers
Know when fine-tuning beats prompting — and execute it: dataset curation, LoRA training on open-weight models, and honest cost-benefit analysis against API baselines.
Why Aizecs
The Aizecs Difference in an Overhyped Market
Every résumé now says 'GenAI'. Our vetting separates engineers who have operated LLM systems under real traffic from those who once called an API in a tutorial.
Production LLM vetting
Candidates walk us through a shipped LLM system — architecture, eval methodology, incident stories. Fewer than 1 in 20 applicants clear the bar.
You interview the engineer
Named profiles with verifiable history. The person you grill on retrieval strategy is the person committing to your repo the following week.
Trial week, our risk
A paid week of real sprint work before any ongoing commitment. Wrong fit costs you one week and zero replacement fees.
Your working day, not theirs
Shifted schedules deliver 4+ hours of US Eastern overlap and near-complete EU/UK coverage — pairing sessions and demo reviews happen live.
IP locked down properly
US-enforceable MSA with IP assignment and NDA. Engineers work inside your repos and your access controls; prompts and datasets never leave your systems.
Half the price of AI marketplaces
Toptal and Turing bill $90–$180+/hr for AI specialists drawn from the same talent pool. Our senior generative AI engineers run $45–$80/hr.
Engagement Models
Three Ways to Bring GenAI Talent On Board
Match the engagement shape to where you are — first LLM feature, scaling an existing product, or standing up a dedicated AI capability.
Embedded specialist
One senior generative AI engineer joins your existing squad, working your backlog under your leads' direction.
Best for: Adding LLM expertise to a strong product team
GenAI pod
Two to four engineers covering application, retrieval, and evals — a self-sufficient unit that owns an LLM workstream end-to-end.
Best for: Shipping a flagship AI product line
Fractional LLM lead + team
A staff-level lead sets architecture and eval standards while mid and senior engineers execute, reporting into your CTO or VP Engineering.
Best for: Teams without in-house AI leadership
Compare Your Options
Hiring Options for Generative AI, Side by Side
Where Aizecs sits against AI-specialist marketplaces, freelance platforms, and building an in-house GenAI team from scratch.
| Aizecs | Toptal / Turing | Upwork | US In-House | |
|---|---|---|---|---|
| Senior GenAI engineer rate | $45–$80/hr | $110–$160/hr | $50–$90/hr | $180–$230/hr loaded |
| Production LLM verification | Shipped-system deep dives | General AI screening | Self-reported | Your interview loop |
| Time to first commit | ~2 weeks | 2–4 weeks | Days, unverified | 4–6+ months |
| Downside if it's a bad fit | One trial week, free swap | Partial refund terms | Sunk cost | Severance + restart |
| Contract flexibility | Month-to-month, 30-day notice | Monthly + platform fees | Per project | Permanent headcount |
FAQ
Common Questions
How quickly can I hire generative AI engineers through Aizecs?
You'll receive 2–4 named profiles within 48 hours of the intro call, each with production LLM work you can verify. After your interviews, the paid trial week usually starts within 3–5 business days — roughly two weeks from first call to first merged PR.
What does it cost to hire generative AI engineers from India?
Mid-level generative AI engineers run $28–$50/hr, seniors $45–$80/hr, and staff/lead LLM engineers $55–$85/hr. That reflects the 20–30% premium AI specialists carry over generalists, yet still lands 40–60% below Toptal or Turing's $90–$180+/hr for comparable talent.
How do you verify real LLM production experience?
Candidates present a shipped LLM system and defend its design: retrieval and chunking choices, eval methodology, cost and latency trade-offs, and how it failed in production. Fewer than 1 in 20 applicants pass. Tutorial-level API experience doesn't clear the bar.
Do your engineers cover prompt engineering and agentic AI, or just RAG?
The full stack. We place prompt engineers who run systematic eval-backed iteration, agentic AI engineers building multi-step tool-using workflows, RAG specialists, fine-tuning engineers, and evals/guardrails engineers. Tell us the shape of your product and we match the specialization.
Which models and frameworks do they work with?
Claude and GPT APIs day-to-day, plus open-weight models where self-hosting makes sense. Typical stacks include LangGraph and similar agentic frameworks, pgvector and Pinecone for retrieval, and eval/observability tooling like Langfuse or Braintrust. They adapt to your stack — you're not buying a framework opinion.
Who owns the prompts, fine-tuned weights, and code?
You do, entirely. IP assignment and NDA sit under a US-enforceable master services agreement, and engineers work exclusively in your repositories under your access controls. Prompts, datasets, eval suites, and model artifacts are your property from the first commit.
What if the engineer isn't a fit after the trial week?
You pay for the trial week and nothing else — no placement fee, no notice period. We line up a replacement candidate immediately at no cost. After a successful trial, the engagement runs month-to-month with 30 days' notice to wind down.
Put a Generative AI Engineer in Your Sprint Next Week
Describe the LLM product you're building and interview named, production-proven engineers within 48 hours.
The paid trial week means your worst case is one week's cost — and your best case is shipping the roadmap you've been sitting on.
30 minutes, zero obligation. If your problem doesn't need a dedicated GenAI engineer yet, we'll say so.