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.