Job Description – Lead Quality Engineering — AI-Driven QA

Location: Dehradun (Onsite)

Experience required: 8–10 years

Role Overview

We are hiring a Lead Quality Engineering & Automation leader to build the AI-agent-driven QA function at Rezolve.ai. The goal is concrete: most testing — from test selection, to execution, to triage — is performed by AI agents, with humans designing the system, curating data, and reviewing outcomes.

You will own the tooling, processes, datasets, and intelligence that let QA agents (a) run automatically on every PR merged into long-living branches, (b) select the right tests for a given change, (c) report results back to engineers in a way that is fast, accurate, and actionable, and (d) continuously learn from production failures and customer-found defects.

Key Responsibilities

AI-Agent-Driven QA Platform

• Design, build, and operate the platform that runs QA AI agents on every PR merged to long-living branches (main, release/*, integration branches), and post structured, actionable results back to the PR and to engineers.

• Define how agents pick up context for a change — diff, services affected, recent incidents, related specs — and how they decide what to do (smoke, regression, exploratory, contract, data-quality, security checks).

• Maintain a growing library of agentic test capabilities: API agents, UI agents (Playwright / browser-use / equivalent), data validators, contract checkers, conversation/AI-evaluation agents, and self-healing locators.

• Set the bar for agent reliability: low flakiness, deterministic reporting, clear pass/fail/needs-human signals, and full traceability of what the agent did and why.

Intelligent Test Selection & Tagging

• Build the test selection intelligence that, given a PR on a specific service, identifies the right subset of tests to run — based on code/path impact, dependency graph, historical defect signal, and ownership.

• Own the test tagging taxonomy (by service, feature, persona, risk, data dependency, latency class, environment) and the automation that keeps tags accurate as code evolves.

• Maintain a high-signal test catalog: every test discoverable, classified, owned, and measured for value (defect-find rate, runtime, flake rate).

• Drive >90% effective coverage on critical flows and regressions — measured by risk-weighted coverage, not just line coverage — and ensure nothing falls through the cracks between unit, integration, e2e, and production checks.

Test Data & Environment Management

• Build good, versioned test datasets — synthetic, anonymized, and production-derived — with clear provenance, refresh cadence, and privacy controls (GDPR, HIPAA).

• Own data seeding, teardown, and isolation strategies so agents can run in parallel without polluting each other or shared environments.

• Partner with Platform/DevOps on ephemeral environments, preview deployments (Vercel, Supabase branches), and reproducible test infrastructure.

CI/CD Integration & Developer Experience

• Embed QA agents as first-class quality gates in CI/CD (GitHub Actions / Azure DevOps / equivalent) on PRs and merges, with right-sized scope per stage (PR check vs. nightly vs. release candidate).

• Make agent output engineer-grade: precise failure summaries, repro steps, suspected root cause, linked logs/traces, and suggested fixes — not raw test dumps.

• Drive down time-to-feedback on PRs and time-to-triage on failures; eliminate flakiness as a first-class metric.

Learning Loop from Production

• Build the loop that learns from production support failures, escalations, and customer-found defects: every notable production issue must result in a new or strengthened test, a tagging improvement, or a process change — tracked end-to-end.

• Partner with Support, SRE, and Engineering on incident reviews; convert RCAs into agent capabilities, datasets, and selection rules.

• Continuously analyze where agents missed and where they over-fired; tune prompts, models, tools, and selection heuristics accordingly.

Quality Strategy, Process & Compliance

• Own the overall QA strategy, quality metrics, and release readiness across products; publish a clear quality dashboard (coverage, escape rate, flake rate, MTTR for test failures, agent precision/recall).

• Establish scalable QA processes, standards, and review practices that hold up under SOC 2, ISO 27001, GDPR, HIPAA audits — including evidence of test execution and approval for regulated changes.

• Coordinate performance, security, and penetration testing efforts with the right specialist partners.

Team, Tools & Innovation

• Lead, mentor, and reshape the QA team into a quality engineering team — engineers who build agents, tools, and datasets rather than execute manual scripts.

• Continuously evaluate and pilot new AI testing tools, frameworks, and models; bring innovation into the org with rigor.

• Partner with Product, Engineering, Architecture, and Security from design through release; identify quality risks early.

Required Skills & Experience

• 8–10 years in software testing / quality engineering, with 3+ years leading QA.

• Strong engineering background: comfortable reading and writing code (TypeScript/JavaScript, Python, or similar), reviewing PRs, and building tools — not only writing test cases.

• Hands-on with modern test automation: Playwright (preferred), Cypress, Selenium, REST-assured / supertest, Postman/Newman, contract testing.

• Demonstrated, recent use of AI-assisted and agentic testing tools (e.g., Claude Code, browser-use, AI test generators, self-healing frameworks) — bring concrete examples of what you built and what it replaced.

• Strong CI/CD integration experience (GitHub Actions, Azure DevOps, Jenkins) and PR-driven quality gates.

• Experience designing test selection / impact analysis systems based on code change, dependency graphs, or historical signal.

• Solid grasp of test data management — synthetic generation, anonymization, fixtures, versioning, and privacy (GDPR/HIPAA-aware).

• Strong understanding of SDLC, Agile/Scrum, and how QA fits into a high-velocity SaaS release model.

Nice to Have

• Experience testing conversational AI, agentic AI, or LLM-based products — evals, golden sets, regression of non-deterministic outputs.

• Experience building or contributing to in-house QA platforms / dashboards.

• Exposure to performance testing (k6, JMeter), security testing, or chaos engineering.

• Familiarity with Vercel, Supabase, AKS, and Postgres-based stacks.

What Success Looks Like

• Every PR merged into a long-living branch is tested by QA AI agents, and engineers receive clear, actionable, low-noise feedback within target SLAs.

• A test selection system reliably picks the right tests per change; full-suite runs are reserved for the right cadence, not every PR.

• Risk-weighted coverage on critical flows ≥ 90%, with a defendable definition and dashboard.

• Production escape rate trends down quarter over quarter; every notable production issue produces a tracked improvement in agents, tests, datasets, or process.

• Flakiness is a managed, declining metric — not background noise.

• Audit evidence for test execution and release readiness is largely automated and consistently passes SOC 2 / ISO 27001 / HIPAA reviews.

• The team operates as quality engineers building AI-driven systems, not as manual testers.

What We Offer

• A rare opportunity to design an AI-agent-first QA function from the ground up.

• High autonomy, strong engineering culture, and explicit investment in AI tooling.

• Competitive compensation and a path into broader quality / engineering leadership.

How to Apply

Along with your CV, please share the following in your response — applications without these details will be deprioritized:

• LinkedIn profile URL.

• Hackathons / open-source / side projects you have participated in or built, with links where possible.

• Specific work you have done in the areas called out in this JD — building AI-agent-driven QA, PR-level test automation on long-living branches, intelligent test selection / impact analysis, test tagging taxonomies, test data management, learning loops from production failures, and concrete examples of using AI tools / agents (Claude Code, browser-use, AI test generators, self-healing frameworks, etc.) in your testing work.

• Why you want to join Rezolve.ai — what specifically draws you to this role and our AI-agent-first QA vision.