Quality Engineer at Evertec ensuring software product quality through functional and automated testing. Collaborating closely with development teams in São Paulo.
About the role
- Principal Engineer – QA and Automation focusing on validating AI-generated code in a hybrid environment. Working on verification systems and strategies to ensure compliance and quality in software delivery.
Responsibilities
- As Principal Engineer – QA and Automation you will be focused on:
- Designing and building automated verification systems that validate AI-generated code at scale — ensuring agent output meets correctness, integration, and compliance standards before it ever reaches a human reviewer.
- Architecting oracle-based testing strategies — capturing reference behaviour from existing systems and using it to verify that new implementations produce equivalent results. You’ll build the infrastructure that makes behavioural equivalence testing continuous and automated.
- Defining risk-calibrated verification strategies across the platform — payroll calculations and legislative compliance demand near-zero-defect rigour, while UI and infrastructure changes can tolerate lighter-touch approaches. You decide what level of scrutiny each change type gets.
- Building cross-service integration testing infrastructure that catches failures across domain boundaries. When AI agents generate code that compiles and passes unit tests but breaks at the seams, your systems catch it.
- Designing and maintaining comprehensive scenario corpora derived from real customer data, legislative requirements, and edge cases — the living test suites that define “correct” for every release.
- Building nightly regression harnesses that run the full scenario corpus, automatically triage failures, and surface actionable diagnostics.
- Defining the quality gates in our delivery pipeline — no code (human or AI-generated) reaches production without passing through your verification layers. You own the “fit to ship” decision framework.
- Establishing property-based and golden-master testing for deterministic domains — payroll calculations that must always produce the same output for the same input, tax tables that must match published rates, leave entitlements that must comply with legislation.
- Building the test infrastructure that enables continuous delivery — test data generators, environment provisioning, parallel execution, and reporting that scales with the pace of AI-assisted development.
- Mentoring engineers on verification-first thinking — shifting the team’s mindset from “testing after the fact” to “defining correctness before generation begins.”
Requirements
- Deep experience designing test automation at scale — framework architecture, not just test writing.
- Strong understanding of verification strategies beyond unit testing — behavioural equivalence, contract testing, golden-master testing, integration testing across distributed systems.
- Experience embedding quality gates into CI/CD pipelines — you know how to make the build pipeline enforce correctness without becoming a bottleneck.
- Proficiency in at least one of: C#/.NET, TypeScript/Node.js, or Python for building test tooling and automation frameworks.
- Experience testing event-driven or event-sourced systems — you understand the challenges of verifying systems where state is derived from streams of events rather than direct database queries.
- Strong analytical skills — you can look at a complex, regulated domain (payroll, compliance, financial calculations) and decompose it into a set of verifiable properties and scenarios.
- Experience with end-to-end testing frameworks (Playwright or similar) and the infrastructure to run them reliably at scale.
- Track record of building test data management solutions — scenario corpora, synthetic data generation, environment seeding — that enable comprehensive regression testing.
- Understanding of software quality beyond code coverage metrics — you think in terms of correctness, equivalence, confidence, and risk.
- Excellent communication skills — you can articulate quality risk to engineering leadership and translate compliance requirements into testable acceptance criteria.
- Nice to have: Experience validating the outputs of AI or autonomous systems — you understand how to build confidence in non-deterministic code generation and have thought about what “correct” means when agents write code.
- Background in regulated domains (payroll, finance, healthcare) where calculation correctness and legislative compliance are non-negotiable.
- Experience with property-based testing, mutation testing, fuzzing, or formal verification techniques.
- Familiarity with observability-driven quality — using distributed tracing, structured logging, and production metrics to complement pre-deployment testing.
- Experience defining quality strategies for platforms undergoing a major technology transformation or replatforming effort.
Job title
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Degree requirement
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Tech skills
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