Integrate automated validation into CI/CD pipelines and communicate actionable findings on quality trends, coverage gaps, and release risks.
Design and build AI agents and agentic QA workflows where appropriate to accelerate test planning, test development, execution, failure analysis, and reporting.
Audit AI-generated outputs against tests and source evidence, continuously improve accuracy and reliability, and measure impact through reduced engineering effort, earlier defect detection, and faster delivery.
Drive cross-team quality improvements, contribute to test architecture and code reviews, and share expertise in testing, debugging, and AI-powered engineering practices.
What we need to see
Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
3+ years of relevant experience in software development or test development for cloud services, infrastructure software, or distributed systems.
Strong programming skills in Python, working proficiency in shell scripting, and experience developing maintainable automation frameworks or engineering tools.
Solid understanding of Linux, networking, HTTP/REST APIs, and distributed-system behavior, including concurrency, timeouts, retries, consistency, and failure recovery.
Hands-on experience with containers, Kubernetes, and CI/CD pipelines, including workload deployment, troubleshooting, and automated regression testing.
Experience designing reproducible tests and diagnosing failures across software and infrastructure components using logs, metrics, and other technical evidence.
Hands-on experience building AI agents or LLM-powered automation using tools, APIs, and relevant context, with the ability to audit outputs for accuracy and systematically improve their quality.
Ability to independently drive test planning and development, prioritize automation opportunities based on engineering needs and business value, and collaborate effectively across teams, with strong written and spoken English.
Ways to stand out from the crowd
Experience building simulation, emulation, or fault-injection tools for infrastructure testing.
Knowledge of resource scheduling, workload placement, hardware topology, or heterogeneous GPU computing environments.
Experience validating multi-tenant services, traffic management, or model-serving infrastructure, including performance and reliability at scale.
A track record of sustained AI initiatives and innovation, turning experiments into adopted solutions with measurable improvements in productivity, product quality, or business outcomes.
Demonstrated technical influence through reusable tooling, cross-team engineering improvements, and knowledge sharing.
