Principal Quality Engineer - Argentina

At Globant, we are working to make the world a better place, one step at a time. We enhance business development and enterprise solutions to prepare them for a digital future. With a diverse and talented team present in more than 30 countries, we are strategic partners to leading global companies in their business process transformation.

We seek a Principal Quality Engineer, who shares our passion for innovation and change. This role is critical to helping our business partners evolve and adapt to consumers personalized expectations in this new technological era.

About the role

The Principal Quality Engineer is a hands-on consultant who owns quality for a client account: understands the client's product, risks and delivery constraints, designs a testing approach to fit them, and delivers it with AI agents doing the execution while they keep the judgment. They define what deserves testing, validate requirements before code, govern the automation and the pipeline, and turn results into release decisions the client trusts.

We work with an orchestrated model in which specialised AI agents explore the application, analyse requirements, design and run tests and investigate defects. We hire for the ability to learn them fast and to design solutions on the same principles with any toolset.

What you will do

• Understand the client's challenge and design a tailored testing solution: scope, risk-based strategy, test levels, automation approach, quality gates and release criteria.

• Review user stories and acceptance criteria with product owners for coherence, completeness and testability; prevent defects upstream in refinement and design reviews.

• Use AI agents and assistants to generate scenarios, run end-to-end and API tests and classify failures — and curate, validate and correct their output before it counts.

• Operate and extend automation suites (Playwright, Appium, API tests) integrated in CI/CD with reliable gates; keep them fast and stable, deciding when to trust self-healing and when not to.

• Triage findings (product defect, test, data or environment), run root-cause and trend analysis, and report real status to the client in business language.

• Define how to validate AI-powered features whose output is not deterministic: success criteria beyond pass/fail, golden datasets, regression when prompts or models change.

• Mentor the engineers around you on evidence-based quality decisions and on reviewing AI-generated work.

Must-have skills and experience

+7 years in software testing or quality engineering, including ownership of test strategy or automation for a team or account. Advanced English (B2/C1).

Consulting and governance

• Has designed test strategies and plans from a client's risks and constraints, and can explain what was left out and why.

• Works requirements with product owners (acceptance criteria, Definition of Ready / Done, BDD / Gherkin as a shared language), leads defect triage and reports quality metrics (coverage, escape rate, flakiness, cycle time).

• Communicates clearly with client stakeholders; negotiates scope when constraints are real.

Engineering

• Reads, adapts and reviews test code (Python, TypeScript, etc); writes fixes and utilities. Judgment over generated code matters more than authorship speed.

• Hands-on with a modern web automation framework (Playwright ie) and mobile automation concepts (Appium); API testing with REST and auth flows (OAuth2 / JWT) using Postman, Bruno, Karate or similar; SQL for data validation.

• Git with pull requests; integrates tests in CI/CD (GitHub Actions, Azure DevOps, GitLab CI or Jenkins) and defines quality gates.

AI and agentic foundations

• Daily use of at least one AI coding assistant (GitHub Copilot, Cursor, Claude Code or similar) to generate, refactor and review test assets.

• Prompt and context engineering in practice: structures prompts and supporting context, versions them, and knows when the prompt — not the product — caused a result.

• Understands how LLMs and agents behave (non-determinism, hallucination, tool use, cost) and reviews AI-generated artifacts before relying on them.

• Has adopted new agent tooling quickly (for example MCP-based tools) or can show a track record of fast tool adoption.

Nice to have

• Evaluation of LLM or RAG applications (DeepEval, Ragas, Promptfoo or custom harnesses; LLM-as-a-judge; red-teaming basics).

• Agent-mode workflows, MCP servers or spec-driven development; observability as a quality signal (Datadog, Grafana, OpenTelemetry).

• Performance (k6, JMeter) or accessibility (WCAG, axe) testing; Docker and cloud test environments.

• ISTQB CT-GenAI or CT-AI; domain depth in financial services, media / OTT, retail, life sciences or enterprise platforms (SAP, Salesforce).