Lead SDET (AI)

Company : Zealant Consulting Group

Job Description - Lead SDET (AI)

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:

6+ 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 organisation-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 up-skill 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 organisation-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

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