AI-QA Talent Network
Deploy vetted AI-QA and Agentic QE engineers from India. Trained on evals, RAG QA, Playwright, API automation, and release-risk workflows. Available through GKO's network.
Who It Is For
- You need additional AI-QA capacity but do not want to build internal headcount yet
- Your internal team has technical leadership but needs execution capacity
- You want vetted specialists without managing a long recruiting process
- You prefer talent that integrates into your team, not a managed engagement
- You need flexibility on team size as project scope evolves
AI-QA talent is hard to find through traditional recruiting
AI-QA is still an emerging discipline. Engineers who combine automation depth, AI evaluation expertise, and production engineering rigor are rare in the open recruiting market.
Traditional staffing firms do not solve this. They source generalists. They cannot distinguish between someone who has read about evals and someone who has built and operated them.
GatekeeperOps Talent Network solves this through a five-stage vetting process designed specifically for AI-QA skill. The bar is the moat.
What You Get
| Deliverable | Description |
|---|---|
| Vetted engineers, ready to deploy | Pre-screened across all five vetting stages |
| Tier-matched profiles | Tier S Lead, Tier A Senior, or Tier B Mid based on requirements |
| Embedded or pod-managed delivery | Engineers can join your team or operate as GKO-managed pod |
| Engineering profile briefs | Skills, project history, technical depth, communication assessment |
| Engagement continuity support | Replacement options from the network agreed in engagement terms |
| Monthly performance check-in | Practice lead hosts brief check-in to verify fit |
Tier Structure
| Tier | Best For |
|---|---|
| Tier S Lead | Senior practitioner work, AI-QA leadership, client-facing capability |
| Tier A Senior | Independent delivery on AI quality, agentic workflows, automation |
| Tier B Mid | Execution support with strong fundamentals, AI exposure |
How It Works
Step 01: Requirements call
30-minute call. Scope, tech stack, tier, timeline, working hours overlap.
Step 02: Profile shortlist
For active network roles, matched profiles within 5 working days. For specialized requirements, sourcing and vetting timelines agreed upfront.
Step 03: Client interviews
You interview shortlisted candidates directly.
Step 04: Placement
Engineer joins within agreed timelines.
Step 05: Onboarding and check-in
First-week onboarding support. Monthly check-in for first 90 days.
Investment
Talent engagement terms are shared privately after understanding your role requirements, seniority level, working hours overlap, project duration, and whether you need embedded engineers or a GKO-managed pod.
Success Metrics
Your team has the AI-QA capacity it needed without the open-market recruiting cycle.
The placed engineer integrates without ramp-up friction.
Your team can scale capacity up or down as project scope evolves.