Lead Quality & Automation Engineer

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.