GenAI QA Architect (Adoption Leader)

N-iX is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join N-iX and become part of a team where your ideas make a real impact.

We’re building a small GenAI Adoption team (GenAI Value Lab) to help delivery projects use AI in real work — not in side experiments.

This role is the QA/Testing specialist: you design how GenAI fits into testing, prove it on real projects, and enable account teams to run it themselves. You don’t own their frameworks long-term — you enable, then move on to the next team.

We’re not looking for someone who “knows it all.” GenAI in QA is still new. Part of your job will be to experiment, break things (safely), learn, and share what actually works. If you’ve played with tools like Copilot, Claude, Cursor, or LLMs in platforms like LambdaTest etc for test generation/log analysis – and you’re excited to turn that into real practice for real teams – you’ll feel right at home.

If you’re a QA Architect / Senior QA who wants to practice GenAI every day, work across multiple stacks, and help hundreds of engineers upgrade how they test, this is that role.

Responsibilities: 

1. GenAI + QA workflows: 

• Define and document testing strategies for GPT/LLM-based features (functional, non-functional, ethical).
• Design LLM-specific test plans, cases, and data: prompt variants, hallucination detection, safety/guardrail checks, response consistency.
• Set quality gates and acceptance criteria for LLM outputs (accuracy, safety, fairness, UX).
• Identify and pilot AI-specific testing tools (e.g. DeepChecks, AI Fairness 360, MLflow/TFX evaluation, prompt/adversarial testing tools) and prepare them for handover to project teams.

2. Tooling, platforms & pipelines:

• Prototype and refine utilities, prompt libraries, and test templates that plug into existing stacks (Selenium/Cypress/Playwright, API test frameworks, performance tools, CI pipelines).
• Build a strategic view on platform vs custom: e.g. LambdaTest / HiRafie–style suites vs Cursor/Claude Code and other IDE-centric tools — and recommend integration paths per account.
• Integrate LLM testing workflows into CI/CD with DevOps/MLOps (sanity checks, regression, evaluation runs, quality gates).
• Validate API integrations (auth, rate limits, error handling, fallbacks, observability around LLM calls).
• Ensure QA practices align with AI governance, data privacy, and responsible AI standards.

3. Enable, don’t own:

• Run workshops, live demos, and pairing sessions with QA/SDET teams — learning by doing, not watching slides.
• Help teams move from “copy-pasting prompts” to structured, documented, and safe AI usage in testing.
• Capture results as GenAI Value Lab playbooks, templates, and case studies that can be reused across accounts.

4. Measure impact:

• Track hard metrics: defect escape rate, regression time, flaky test rate, coverage, lead time, DORA/SPACE.
• Track human impact: less repetitive QA work, cleaner pipelines, higher confidence before release.
• Turn improvements into clear stories that make adoption easier for the next team.

Requirements:

• Proven experience as QA Architect / Test Lead / Senior QA/SDET in a modern SDLC (automation + CI/CD).
• Strong base in:
• Test strategy & test pyramid
• UI/API automation and CI integration
• At least some non-functional testing (performance/reliability/security basics)
• Hands-on with modern QA tooling: e.g. Selenium / Cypress / Playwright, REST API testing, test runners/reporting tools, CI/CD (Jenkins, GitHub Actions, GitLab, etc.).
• Able to read and reason about test code (Java, C#, Python or similar).

GenAI understanding:

• You’ve already used GenAI for real work: generating tests, refactoring test code, analyzing logs, writing docs, etc.
• Understanding of LLMs, prompt engineering, and GenAI workflows (you don’t have to be a researcher, but you’re not starting from zero).
• Experience or strong interest in:
• Testing GPT/LLM-based features
• Handling hallucination, bias, and safety risks
• Designing tests for “approximate” outputs, not just exact matches
• Skilled in or willing to learn Python/PyTest/Postman and evaluation tooling (bonus: LangChain, OpenAI APIs, model evaluation frameworks, fairness/bias tools).

How you work:

• Comfortable leading change: introducing new tools/processes, showing value with real numbers, not buzzwords.
• Able to tailor solutions to different client rules, compliance needs, and tech stacks.
• Clear communicator who can talk to QA, devs, architects, and managers without turning it into a research lecture.

How GenAI Value Lab works (and why this is interesting)

• We don’t replace project QA teams or run their regression suites forever.
• We do:
  
  • Design patterns and guardrails
  • Prove them on 1–2 projects
  • Turn them into reusable playbooks and move to the next account

You’ll work across multiple accounts and domains, helping up to 1,700 engineers and dozens of QA teams change how they test — while building your own GenAI expertise much faster than staying in a single product team.

If you’re a QA architect who wants to be on the front line of how testing changes with GenAI — and you enjoy enabling many teams more than owning one codebase — we’d like to talk.