Director: AI Systems Reliability, Testing & Performance

Novartis Farmacéutica; Greater London; Hybrid; GBP 180,000 - 240,000; Full time

Job summary

Novartis in London seeks a Director of AI Systems Reliability, Testing & Performance to define how AI systems across the Development portfolio are evaluated, tested, validated, and monitored for production-readiness.

You will lead reliability engineering, adversarial testing, drift detection, and observability, providing the technical evidence needed to ensure AI capabilities are robust, secure, and fit-for-use over time.

Responsibilities

• Define how AI systems across Novartis Development are evaluated, tested, validated, benchmarked, monitored, and continuously improved throughout their lifecycle.
• Provide technical leadership in AI evaluation science, reliability engineering, validation, adversarial testing, and performance assessment.

Job description

Director: AI Systems Reliability, Testing & Performance#LI-Hybrid Location: London

Novartis is unable to offer relocation support for this role: please only apply if this location is accessible for you. The Director, AI Systems Reliability, Testing & Performance is a senior technical AI leadership role within Data Science & AI, responsible for defining how AI systems across Novartis Development are evaluated, tested, validated, benchmarked, monitored, and continuously improved throughout their lifecycle. This role owns the technical evidence required to determine whether AI systems are reliable, robust, secure, and fit-for-use across Novartis Development. The role defines common approaches for AI evaluation, benchmarking, testing, validation, monitoring, and production-readiness across agentic AI systems, predictive models, retrieval systems, digital twins, and other AI capabilities. The Director provides technical leadership in AI evaluation science, reliability engineering, validation, adversarial testing, and performance assessment. Key areas of focus include model and agent evaluation, benchmarking, failure-mode analysis, drift detection, digital twin validation, AI red teaming, observability, traceability, and technical evidence generation supporting regulated and business-critical AI systems. This is not a Governance, Product Management, PMO, or infrastructure operations role. Governance owns policies, risk frameworks, and approval processes. Product teams own roadmaps, adoption, and value realization. DDIT and engineering teams own platforms, infrastructure, and operational services. Success means Development AI systems are supported by objective evidence demonstrating how they perform, where they fail, and whether they remain fit-for-use over time.