SDET / Senior AI QA Solutions Engineer

About the Role We are looking for a hands-on Middle+ AI QA Platform Engineer to join the core team building a new AI-driven QA orchestration platform. This is not a classic QA automation role focused on writing Selenium, Appium, or API tests. The main focus is platform engineering: building components for test execution orchestration, queue and worker execution, device and resource management, runtime coordination, reporting integrations, and future agentic recovery workflows. The role keeps a strong connection to QA because the platform is intended to support test execution, test coverage intelligence, test result analysis, automation scaling, and reduction of manual QA effort. However, the engineer is expected to contribute primarily as a backend/platform engineer, not as a traditional test automation engineer. You will work under the technical guidance of the Automation Architect and collaborate with R&D engineers, QA automation engineers, SDET specialists, QA teams, and platform stakeholders. The architecture direction will be provided, while your responsibility will be to turn that direction into reliable, maintainable, production-oriented platform functionality. The roadmap includes trigger APIs, queue and worker models, orchestration runtime, device setup planning, cloud device farm integration, device gateway APIs, distributed worker execution, smart scheduling, artefact collection, result analytics, integrations with QA reporting systems, and agentic recovery capabilities. The project context includes both Java and C#/.NET. The broader platform direction is expected to involve Java, while the first project phase includes C#/.NET and Unity-related work around Automation Layer API changes and WebSocket/proxy behaviour. Experience with both languages is an excellent signal. Strong experience in either Java or C#/.NET is acceptable if the candidate has the right platform engineering, AI, and orchestration depth. We are open to candidates from QA, SDET, test automation, backend, platform, tooling, developer productivity, or AI engineering backgrounds. QA/testing domain experience is a strong advantage, but it is not mandatory if the candidate has strong engineering skills in Java and/or C#/.NET plus relevant experience with AI agents, orchestration, distributed execution, or platform development. Production experience with AI agents or LLM orchestration is a strong plus. Pet projects, internal prototypes, research projects, and personal tools are also valuable if the candidate can clearly explain the architecture, implementation choices, failure handling, state management, observability, and practical limitations. What You Will Do Platform Engineering - Build and extend backend/platform components for an AI-driven QA orchestration platform. - Implement platform functionality around trigger APIs, execution flows, queue and worker models, runtime orchestration, and job lifecycle management. - Develop reliable service interfaces, REST APIs, WebSocket-related integrations, and internal platform contracts. - Contribute to resource lifecycle management, including device setup planning, leasing, cleanup, and execution coordination. - Work with existing architecture and extend it safely without breaking surrounding automation and execution flows. - Write maintainable, testable, production-oriented code with clear ownership of reliability, error handling, and debuggability. AI Agents and Orchestration - Contribute to platform capabilities involving AI agents, LLM-assisted workflows, tool calling, agentic recovery, and automated execution support. - Help design and implement agent workflows that can observe state, collect evidence, propose recovery actions, or support automation maintenance. - Build or integrate orchestration patterns for multi-step AI-assisted execution flows. - Reason about agent state, retries, guardrails, failure modes, human control points, and validation of AI-generated outputs. - Evaluate where AI agents can reduce manual QA effort and where deterministic platform logic is required. Queue, Worker, and Distributed Execution - Implement or extend queue-based execution flows, worker models, schedulers, and background processing components. - Support parallel execution and distributed worker coordination. - Contribute to execution state tracking, retries, cancellations, timeouts, leases, cleanup flows, and failure recovery. - Help optimise execution throughput, stability, and observability as the platform scales. QA Platform and Test Execution Domain - Build platform capabilities connected to test execution, test coverage, automation scaling, smart scheduling, reporting, and result analysis. - Integrate with QA-related systems such as Automation Hub, TestRail, Jira, Allure-like reporting systems, or similar tools. - Contribute to enhanced artefact collection, execution progress tracking, result links, an What You Bring - Strong SDET, software engineering, or test automation engineering background with the ability to read, review, validate, and improve production-oriented code. - Strong understanding of unit testing, testability, code quality, and maintainable test architecture. - Hands-on experience with software development and/or SDET engineering, including practical work with automation frameworks and testing ecosystems. - Strong engineering judgement to identify technically correct but poorly designed, unmaintainable, or low-value AI-generated solutions. - Unity/C# development and Unity unit testing experience is considered a strong priority for this role, as part of the platform scope includes validation and unit test coverage for Unity-based implementations. - Candidates without direct Unity experience may also be considered if they have strong C# / Java / C++ development or SDET experience and can demonstrate solid code validation, unit testing, testability, and test architecture skills. - Practical experience using AI coding tools and AI-assisted development workflows. - Ability to decompose complex engineering tasks into clear steps suitable for AI-assisted execution. - Understanding of AI agent context management, prompt iteration, and validation feedback loops. - Proven experience working with automation frameworks and testing ecosystems. - Ability to work effectively in ambiguous R&D environments with evolving requirements. - Strong communication skills and ability to share practical engineering findings with architects, QA, and development teams. - English level: B2 or higher. A practical technical assessment may be used to validate the candidate’s ability to review AI-generated code, assess unit test quality, and reason about testability and maintainability. Tech Stack & Skills - Automation & Testing - Automation framework design and maintenance. - Unit testing and automation testing practices. - Testability, code quality, and unit test validation. - API and mobile automation testing ecosystems. - RestAssured. - Appium. - AI-Assisted Engineering - AI coding agents and orchestration tooling. - Prompt engineering and context management. - Task decomposition for AI-assisted execution. - Spec-Driven Development or similar structured workflow approaches. - AI-generated code validation and iterative feedback loops. - Agent execution patterns, feedback cycles, and workflow optimisation. - Primary Engineering Context - Unity / C# development and unit testing. - Code review and AI-generated code validation. - Production-oriented code validation. - Maintainability, scalability, and engineering quality assessment. - Engineering Tools - Git-based workflows. - CI/CD pipelines. - Code review and quality validation practices. Nice to Have - Experience building internal engineering platforms or developer tooling. - Experience with automation framework migration initiatives. - Familiarity with GitHub Spec Kit or similar Spec-Driven Development tooling. - Experience working in gaming or live-service environments. - Experience with AI orchestration frameworks such as LangChain, AutoGen, or similar. - Ability to provide technical guidance, conduct reviews, and share AI-assisted engineering practices with other team members as the platform matures. What We Offer - Stability: Dedicated long-term work on a single innovative project. - Team: Collaboration with strong, mature QA and engineering teams. - Flexibility: Flexible working hours and remote-first culture. - Growth: Continuous professional growth and learning support. - Innovation: Opportunity to work with cutting-edge AI-assisted engineering technologies and participate in high--impact R&D initiatives. - Impact: A chance to contribute to an early-stage AI-driven QA platform initiative with future scaling potential across -the QA department. About a1qa a1qa is a pure-play software testing and quality assurance company founded in 2003. With more than 1,000 QA professionals worldwide, we help global clients deliver reliable software by combining engineering expertise, mature QA practices, and a collaborative team culture.