Quality Engineering Developer - offshore

What You’ll Do

• Build scalable automation frameworks across UI, API, integration & event‑driven layers
• Act as a quality architect, influencing system design, testability & release readiness
• Embed automated quality gates into CI/CD pipelines
• Engineer quality for distributed systems, microservices, and high‑availability platforms
• Leverage AI / GenAI to accelerate test design, automation development, evaluation, and debugging
• Practice Spec‑Driven Development (SDD) to align specifications, implementation, and validation
• Own end‑to‑end production quality, including release certification & incident triage

What We’re Looking For (Must‑Have)

Core Engineering

• Expert‑level Java / Python (OOP, data structures, design patterns)
• Proven experience building automation frameworks from scratch
• Hands‑on Playwright experience for modern UI automation
• Strong API and integration testing (REST, microservices)
• CI/CD experience with automated quality gates (TeamCity, Jenkins, GitHub)
• Solid understanding & proficiency with SQL, handling backend data stores and performing data validation
• Experience validating high‑availability, low‑latency systems
• Practical, maintainable BDD implementations

Distributed Systems & Reliability

• Strong understanding of microservices and event‑driven architectures
• Experience with performance, load, stress, and resilience testing
• Ability to debug using logs, metrics, and traces
• Using production telemetry to guide quality strategy

AI‑Driven & Spec‑Driven Engineering (Must‑Have)

• Hands‑on use of AI / GenAI tools for test design, automation coding, and debugging
• Strong AI evaluation skills—validating AI‑generated code and tests
• Expertise in prompt engineering and context management
• Proven Spec‑Driven Development (SDD) experience
• Experience using and developing Agents, Skills and Model Context Protocol (MCP) integrations
• Ability to embed AI‑assisted QE workflows safely into CI/CD
• Strong understanding of responsible AI usage and verification of AI outputs

Quality Ownership

• Experience with release certification, regression strategy, and production validation
• Hands‑on incident triage and root‑cause analysis
• Willingness to support production releases, including on‑call rotations
• Partner with product owners, developers, architects, and platform teams to define project-specific quality strategy, acceptance criteria, testability standards, and release-readiness checkpoints.
• Design and maintain end-to-end automated test coverage for critical business journeys across UI, API, microservices, database, integration, and event-driven components.
• Build reusable test utilities, service virtualization, mocks, stubs, synthetic test data, and environment-aware automation to enable reliable execution across development and test environments.
• Integrate automated functional, regression, performance, resilience, and security-focused checks into CI/CD pipelines with clear quality gates and actionable reporting.
• Validate asynchronous workflows, message queues, event schemas, retries, idempotency, failure handling, and data consistency across distributed services.
• Use SQL, logs, metrics, traces, and production telemetry to validate backend processing, diagnose defects, support incident triage, and improve risk-based test coverage.
• Apply Spec-Driven Development by converting requirements and technical specifications into executable tests, traceable validation assets, and measurable quality outcomes.
• Use approved AI / GenAI tools to accelerate test design, automation coding, code review, debugging, and defect analysis while independently validating generated outputs.
• Contribute to framework architecture, coding standards, pull-request reviews, engineering documentation, and continuous improvement of the QE toolchain.
• Own defect quality from discovery through closure, including reproducibility, severity assessment, root-cause collaboration, regression protection, and release risk communication.
• Support release certification, production validation, post-release monitoring, and on-call incident response as required by the project.
• Partner with product owners, developers, architects, and platform teams to define project-specific quality strategy, acceptance criteria, testability standards, and release-readiness checkpoints.
• Design and maintain end-to-end automated test coverage for critical business journeys across UI, API, microservices, database, integration, and event-driven components.
• Build reusable test utilities, service virtualization, mocks, stubs, synthetic test data, and environment-aware automation to enable reliable execution across development and test environments.
• Integrate automated functional, regression, performance, resilience, and security-focused checks into CI/CD pipelines with clear quality gates and actionable reporting.
• Validate asynchronous workflows, message queues, event schemas, retries, idempotency, failure handling, and data consistency across distributed services.
• Use SQL, logs, metrics, traces, and production telemetry to validate backend processing, diagnose defects, support incident triage, and improve risk-based test coverage.
• Apply Spec-Driven Development by converting requirements and technical specifications into executable tests, traceable validation assets, and measurable quality outcomes.
• Use approved AI / GenAI tools to accelerate test design, automation coding, code review, debugging, and defect analysis while independently validating generated outputs.
• Contribute to framework architecture, coding standards, pull-request reviews, engineering documentation, and continuous improvement of the QE toolchain.
• Own defect quality from discovery through closure, including reproducibility, severity assessment, root-cause collaboration, regression protection, and release risk communication.
• Support release certification, production validation, post-release monitoring, and on-call incident response as required by the project.