Lead Software Development Engineer in Test - AI

Accountabilities: Define and lead the overall Quality Assurance (QA) strategy for AI products and engineering initiatives. Architect, develop, and implement large-scale, reusable, and cross-platform test automation frameworks. Take end-to-end ownership of the quality charter for the AI vertical, ensuring consistency, reliability, scalability, and performance across products. Drive organization-wide quality engineering initiatives and establish standardized testing methodologies and best practices. Design and implement scalable testing strategies for AI-powered systems, covering Retrieval-Augmented Generation (RAG), hallucinations, model drift, bias, and other AI-specific quality challenges. Establish testing approaches that evaluate AI model behavior, accuracy, reliability, and consistency under different scenarios and operating conditions. Own key technical decisions related to test strategies, automation tools, frameworks, and AI testing methodologies. Shape and execute the AI quality roadmap, identifying opportunities to reduce technical risks and improve product reliability and scalability. Collaborate with cross-functional teams, including Software Engineering, Product, and Architecture, to align quality initiatives with technical and business objectives. Participate in architectural discussions and influence engineering decisions to ensure quality is integrated throughout the software development lifecycle. Lead, coach, and mentor the QA/SDET team, strengthening technical capabilities and encouraging continuous learning. Establish engineering standards, promote knowledge sharing, and support the development of a strong technical talent pipeline. Monitor quality metrics, identify systemic risks, and implement continuous improvement initiatives across the AI product ecosystem. Requirements: 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. Benefits: Fully remote working arrangement within India. Competitive compensation package. Opportunity to lead AI quality engineering initiatives and influence organization-wide testing strategies. Exposure to advanced AI technologies, model testing methodologies, and large-scale automation frameworks. Opportunity to shape the quality roadmap for innovative AI-powered products. Leadership opportunities, including mentoring engineers and developing high-performing technical teams. Collaboration with globally distributed, cross-functional engineering teams. Opportunities for continuous learning, technical growth, and professional development. Exposure to modern cloud technologies, CI/CD practices, and emerging MLOps capabilities. An innovation-driven environment that encourages ownership, technical excellence, and continuous improvement.