Original listing text, shown exactly as published by the company.
About the Role
We are looking for a Lead Software Development Engineer in Test with deep expertise in AI tools and model testing. This role requires an individual who can define overall QA strategy, architect automation frameworks, and drive organisation-wide quality initiatives for our AI products. You will own the quality charter for the AI vertical, influence cross-functional teams, and play a key role in shaping the AI quality roadmap.
Requirements
- 7+ years of overall experience in QA/SDET roles
- Deep expertise in AI tools & model testing, responsible for designing scalable test approaches for RAG, hallucination, drift, and bias
- Proven experience in architecting large-scale, reusable, cross-platform automation frameworks
- Strong leadership skills to coach and upskill an entire team, set best practices, and build a talent pipeline
- Ability to influence cross-functional teams and participate in architecture discussions
- Strong problem-solving skills with the ability to own decisions on test strategy, tools, frameworks, and AI testing methodologies
Responsibilities
- Define overall QA strategy and architect large-scale, reusable, cross-platform automation frameworks
- Drive organization-wide quality initiatives and own the quality charter for the AI vertical
- Possess deep expertise in AI tools & model testing, responsible for designing scalable test approaches for RAG, hallucination, drift, and bias
- Own decisions on test strategy, tools, frameworks, and AI testing methodologies
- Coach and upskill the entire team, set best practices, and build talent pipeline
- Influence cross-functional teams, participate in architecture discussions, and align testing with business strategy
- Responsible for end-to-end quality strategy at the product/vertical level
- Shape AI quality roadmap, reduce organization-wide risk, and improve scalability and reliability
Bonus Points
- Experience with CI/CD pipelines and cloud platforms (AWS/GCP/Azure).
- Familiarity with MLOps pipelines and monitoring AI systems.
- Exposure to performance and scalability testing of AI workloads.
