Salary Range:
£100,240.00 - £186,160.00
Band
Level 6
Job Description Summary
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.
Job Description
Major Accountabilities
AI Evaluation & Benchmarking
- Define evaluation methodologies for AI systems, models, agents, digital twins, and simulation environments across Development.
- Establish benchmark suites, evaluation datasets, and testing harnesses used across AI initiatives.
- Define objective measures for quality, reliability, robustness, grounding, agent effectiveness, and task success.
- Ensure evaluation approaches remain scientifically rigorous, reproducible, and comparable across AI systems.
- Build a common evidence framework for assessing AI capabilities, limitations, and fitness-for-use.
AI Testing & Validation
- Establish approaches for hallucination testing, failure-mode analysis, robustness testing, and behavioral validation.
- Define validation methodologies for agentic systems, digital twins, simulation environments, and human-in-the-loop workflows.
- Develop production readiness criteria for AI systems operating in Development environments.
- Support technical validation activities required for GxP-relevant and regulated AI systems.
- Ensure AI systems are tested under realistic operating conditions and supported by objective validation evidence.
Reliability, Monitoring & Performance
- Define how AI system reliability, degradation, drift, and operational performance are measured over time.
- Establish monitoring requirements and performance assessment frameworks across Development AI systems.
- Partner with engineering teams to ensure required evaluation, monitoring, tracing, and observability signals are available.
- Define methods for identifying, diagnosing, and assessing AI system failures.
- Promote evidence-based improvement of deployed AI capabilities.
AI Security & Adversarial Testing
- Define approaches for AI red teaming, adversarial testing, and security evaluation.
- Establish testing methodologies for prompt injection, jailbreaks, retrieval attacks, tool misuse, and agent manipulation scenarios.
- Assess AI-specific vulnerabilities and resilience risks in collaboration with cybersecurity teams.
- Ensure security testing forms part of AI system validation and production-readiness assessments.
Technical Evidence & AI Performance Intelligence
- Establish portfolio-wide approaches for capturing technical evidence related to AI quality, reliability, robustness, and performance.
- Define standard reporting for benchmark results, validation findings, monitoring signals, and reliability assessments.
- Create transparency into AI system strengths, limitations, and failure modes.
- Generate technical evidence supporting validation, audit, inspection, and governance activities.
- Enable objective decisions on AI system readiness, reliability, and fitness-for-use.
Key Performance Indicators
· Percentage of AI systems meeting assurance standards.
· Coverage of evaluation and monitoring across AI portfolio.
· Reliability and availability of business-critical AI solutions.
· Audit and compliance readiness.
· Time to identify and resolve AI performance issues.
· Percentage of AI initiatives with measurable business outcomes.
· Leadership confidence in AI performance reporting.
Minimum Requirement: Work Experience
- 10+ years in AI, machine learning, software engineering, quality, risk, validation, or technology governance.
- Experience deploying or overseeing business-critical AI systems.
- Experience establishing governance, controls, quality standards, or operational frameworks.
- Experience defining evaluation approaches and performance metrics.
- Experience managing operational risk in regulated environments.
- Experience partnering with infrastructure and platform teams.
ewards
At Novartis, we’re committed to reimagining medicine together - and rewarding the people who make it happen.
The rewards of being part of our team go far beyond base pay and incentives. We also offer a variety of competitive benefits in kind to help you thrive personally and professionally, such as insurance plans, retirement plans, wellbeing resources and global recognition programs. In addition, we provide flexible and hybrid working options, where possible, and a minimum of 14 weeks paid parental leave.
Expected Annual Base Salary Range for role:
The salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically.
In addition to your base salary, you may be eligible for a performance-based bonus depending on certain performance parameters. Further details will be provided during the application process.
Pay equity is a fundamental principle of our employment policy and reflects our commitment to create a diverse, equitable and inclusive environment that treats all employees with dignity and respect, as outlined in our Code of Ethics.
Read our brochure to learn more about our global total rewards offering: https://www.novartis.com/sites/novartis_com/files/novartis-life-handbook.pdf
Note: Benefits and compensation may vary by country and are subject to local legal requirements, including provisions of collective bargaining agreements where applicable. A full overview of your compensation package, including any relevant collective bargaining agreement details applicable to your role based on your employment location and Novartis employer entity, will be communicated separately to you during the application process.
Commitment to Diversity and Inclusion / EEO paragraph:
Novartis is committed to building an outstanding, inclusive work environment and diverse teams’ representative of the patients and communities we serve.
Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture
Skills Desired
Artificial Intelligence (AI), Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Mentorship, Stakeholder Engagement, Statistical Analysis, Time Series Analysis
