QA Lead (LATAM based)

Arionkoder is an AI consulting firm helping companies build, embed, and own AI systems that drive real business outcomes. We combine Product Development, Artificial Intelligence, and Team Augmentation to craft digital products and solutions that create lasting impact — guided by a human-centric approach and a commitment to pushing the boundaries of what technology can do.

We are looking for a Quality Lead experienced in leading quality efforts - not only for their own testing output, but for the team achieving its quality objectives. This is a hands-on leadership role: you remain a practicing quality engineer, equally comfortable designing manual and exploratory test scenarios as writing and maintaining automated suites in CI, while also setting the quality direction, planning the work, and creating the conditions for others to do their best work.

The QA Lead is the primary quality reference. You define the quality strategy for the products in scope, make or facilitate the key quality decisions, keep visibility over what everyone is working on, surface risks and blockers early, and ensure no significant quality risk stays invisible. You do not need to be the deepest expert in every tool or layer you understand how the pieces fit together and ensure the team's work follows a coherent quality vision.

You bring an AI-first mindset to the craft - using AI tools to accelerate test design, automation, triage, and reporting, with the judgment to know when to trust the output and when not to - and you push that way of working into the team. You also own quality for LLM-powered features (assistants, RAG, agents, etc.) where “pass/fail” is a judgment you have to operationalize through evals: curated datasets, graded criteria, baselines, and regression thresholds.

This is not a role for someone who waits for tickets to be marked "ready for QA", nor for someone who leads only from a spreadsheet. We need a lead who challenges unclear requirements, asks the uncomfortable questions early, coaches others to do the same, and treats production confidence as a team deliverable — measured in fewer surprises in production, faster releases, and a stronger quality culture across the teams you work with.

Key Responsibilities:

• Leadership and Team Accountability: Be accountable for the team achieving its quality objectives - providing direction and fostering ownership, not doing everything yourself.
• Maintain visibility into what each team member is working on, how they are approaching it, and where support is needed.
• Identify blockers, risks, and deviations early, and adjust the quality plan when circumstances change.
• Plan and distribute quality work across the team: scope, sequencing, ownership, and realistic effort estimates.
• Ensure quality decisions are made at the right level with the right people; delegate where possible and resolve issues when the team cannot reach a conclusion.
• Create an environment where team members participate in decisions, challenge ideas, take responsibility, and develop autonomy.
• Evaluate team members against the expectations of their role and seniority; provide actionable feedback and meaningful growth opportunities.
• Improve the team's quality practices, tools, processes, and ways of working by turning experience and feedback into practical changes.
• Quality Strategy and Direction
• Establish and communicate a coherent quality vision for the products in scope; ensure individual decisions and implementations fit together.
• Define test strategies based on product risk, release goals, and business impact - not generic checklists.
• Influence quality upstream by reviewing requirements and designs to surface ambiguity, edge cases, and testability issues early.
• Identify non-functional risks (performance, security, accessibility, data integrity) and ensure they are addressed or escalated with clear owners and mitigation plans.
• Define and maintain the team's quality standards: definition of done, bug hygiene, coverage expectations, documentation.
• Manual QA and Exploratory Testing
• Run and lead structured exploratory sessions guided by risk and user personas.
• Cover what automation can't yet: complex flows, visual checks, new feature shakedowns, third-party integrations.
• Document findings clearly: reproducible steps, environment, expected vs. actual, severity, impact - and hold the team to the same bar.
• Act as a user advocate during pre-release validation.
• Test Case Design and Documentation
• Design and review test cases covering happy paths, edge cases, negative scenarios, role/state transitions, integrations, and relevant non-functional risks.
• Maintain traceability to requirements, acceptance criteria, and product risks.
• Build and maintain smoke tests, regression suites, and release checklists as living, team-owned assets.
• Automation QA
• Guide the automation approach: framework, layers, structure, test data, reporting - and contribute to it hands-on.
• Decide what to automate based on stability, ROI, and risk - and what not to; make that reasoning explicit so the team can apply it.
• Keep flakiness under control as a team-level metric, not an individual annoyance.
• Review automation work from others and raise the bar through review and pairing.
• Defect Management and Quality Reporting
• Own defect triage and prioritization across the team; keep hygiene clean (title, severity, repro, environment, links).
• Produce concise release quality reports: what was tested, what wasn't, known risks, recommendations.
• Communicate quality status, trade-offs, progress, and risks clearly to technical and non-technical stakeholders.
• Surface trends and turn them into actionable improvements.
• Release Readiness and Quality Gates
• Own release validation: smoke, regression, environment checks, sign-off criteria.
• Define and enforce quality gates for staging, UAT, and production.
• Coordinate with DevOps on hotfixes, rollback, and post-release monitoring.
• Make confident go/no-go calls backed by evidence, and stand behind them.
• AI-Augmented QA
• Use AI tools daily to accelerate test case generation, exploratory charters, automation, defect triage, and reporting.
• Apply judgment to every AI-assisted output - treat AI as a force multiplier, not a shortcut.
• Establish how the team uses AI in quality work: what is accelerated, what is always human-reviewed.

Required Qualifications:

Mandatory:

7+ years of professional Quality Engineering / QA experience in modern software products, with demonstrable depth in both manual and automation (not one bolted onto the other).

2+ years leading a QA effort or a team - owning the quality objective for a product or team, planning and distributing work, unblocking others, and being the quality reference people escalate to. Formal line management is not required; demonstrated leadership accountability is.

5+ years designing test cases, test plans, and test strategies for non-trivial systems - covering happy paths, edge cases, negative scenarios, integrations, and non-functional risks.

4+ years building and maintaining automated test suites in production-grade CI environments, with hands-on framework decisions and ownership of flakiness, reliability, and reporting.

Strong experience with at least one modern UI automation tool (Playwright, Cypress, Selenium, Appium, or equivalent) and at least one API testing approach (Postman, REST clients, or automated API frameworks such as RestAssured, Supertest, Pytest+requests, or similar).

Working knowledge of test case management tools (Qase, TestRail, Xray, Zephyr, or similar) and bug tracking tools (Jira, Linear, Asana, or similar).

CI/CD fluency: comfortable wiring automated tests into pipelines using GitHub Actions, GitLab CI, Jenkins, or similar; understands quality gates, parallelization, and reporting.

Scripting/programming proficiency in at least one of: JavaScript/TypeScript, Python, Java, or equivalent — sufficient to write, debug, and refactor automation code without dependency on engineers.

Solid understanding of software testing fundamentals: risk-based testing, equivalence partitioning, boundary analysis, exploratory testing techniques, regression strategy.

Strong release readiness mindset: ability to define quality gates, run release validation, and make confident go/no-go calls backed by evidence.

Planning and estimation: ability to clarify scope, validate assumptions, identify uncertainty, and produce realistic quality plans and estimates with a team.

Coaching and feedback: ability to evaluate others' work against role expectations and give actionable, direct feedback.

Stakeholder communication: ability to explain quality status, risks, and trade-offs clearly to technical and non-technical audiences, including clients.

Excellent written and spoken English; comfortable working in fully remote, distributed teams.

Senior judgment: ability to operate autonomously, challenge unclear requirements, prioritize by risk, and own outcomes without micromanagement.

AI-first working style: hands-on, daily use of AI tools (Claude, Cursor, or similar) to accelerate test design, automation, debugging, and documentation with the judgment to know when to trust the output and when not to.

Preferred:

Experience building or scaling a QA function: hiring, onboarding, defining career expectations, or standing up a QA practice from scratch.

Experience contributing to or building a QA playbook or organization-wide quality standards.

Experience leading quality in a client-facing or consulting context, where quality status and trade-offs must be defended to an external stakeholder.

Performance and load testing experience (k6, Locust, JMeter, or similar) and the ability to design realistic load scenarios.

Security testing awareness: OWASP Top 10, injection patterns, authentication/authorization edge cases.

SQL for data validation; comfort reading logs and traces to debug failures past the UI layer.

Familiarity with observability tooling (Datadog, New Relic, Grafana, Sentry, or similar) for production quality feedback loops.

Experience working with enterprise integrations (GitHub, Jira, Slack, Confluence) and validating contract behavior between systems.

About us

In 2010, our founders dreamed up a company that could positively impact the world. Since then, we've been in permanent evolution: from business consulting to innovation and design, and now to building amazing digital products powered by AI. We scaled globally, launched AI Labs and Reshape Studios, and claimed our space as an AI consulting firm — helping organizations move from pilots to production through accountable AI.

Today, we're expanding our impact across industries, driving real AI adoption through accountability, governance, and value at scale. Dreamers first, builders always.