Lead Software Development Engineer in Test - AI

As a Lead Software Development Engineer in Test, you will take ownership of quality engineering strategy across a rapidly evolving AI-driven product ecosystem. You will define and implement scalable testing approaches, architect reusable automation frameworks, and establish quality standards that support reliable and high-performing AI solutions. A key focus will be evaluating AI models and tools, addressing challenges such as hallucinations, retrieval accuracy, model drift, and algorithmic bias. You will drive organization-wide quality initiatives while collaborating closely with engineering, product, and architecture teams. In addition to providing technical leadership, you will mentor and develop engineering talent, promoting best practices and continuous improvement. This is an opportunity to shape the long-term AI quality roadmap within a global, remote-first, and innovation-driven technology environment.

• At least 7 years of professional experience in Quality Assurance (QA), Software Development Engineering in Test (SDET), or related software testing roles.

• Deep expertise in AI tools and model testing, with demonstrated experience designing testing strategies for complex AI-powered applications.

• Strong understanding of AI-specific testing challenges, including Retrieval-Augmented Generation (RAG), hallucination detection, model drift, bias evaluation, and model reliability.

• Proven experience architecting and implementing large-scale, reusable, and cross-platform test automation frameworks.

• Strong technical leadership capabilities, with experience mentoring engineers, developing team expertise, establishing best practices, and building high-performing teams.

• Demonstrated ability to influence cross-functional stakeholders and contribute to technical and architectural discussions.

• Strong problem-solving and analytical skills, with the ability to independently make decisions regarding testing strategies, tools, frameworks, and AI quality methodologies.

• Experience defining and managing end-to-end quality strategies at the product or organizational level.

• Excellent communication and collaboration skills, with the ability to translate complex technical challenges into actionable quality initiatives.

• A proactive and strategic mindset, with the ability to balance technical execution, team development, and long-term quality objectives.

• Experience with Continuous Integration and Continuous Delivery (CI/CD) pipelines and cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure is an advantage.

• Familiarity with Machine Learning Operations (MLOps) pipelines and monitoring systems for AI-powered applications is a plus.

• Exposure to performance, load, and scalability testing of AI workloads is desirable.