Brief Description: 

​​The Associate Director, Enterprise AI Architect is a senior individual contributor responsible for maturing and leading the AI/ML architecture capability at Jazz. Reporting to the Director of Enterprise AI, this role will partner closely with senior leadership to develop and evolve the enterprise AI architecture strategy – including technology stack decisions, architectural standards, best practices, and AI-related policies and guardrails. The position focuses on strategic architecture and solution design rather than day-to-day model development, ensuring that Jazz’s AI systems are scalable, secure, compliant, and aligned with business needs. The ideal candidate is a pragmatic and intelligent technical leader who is organized, a clear communicator, and demonstrates strong critical thinking, diplomacy, and stakeholder management. They are passionate about the value AI can bring to the enterprise while remaining appropriately cautious about its power, risks, and responsible use. 

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​​Essential Functions/Responsibilities 

• ​Define and continuously enhance Jazz’s enterprise AI/ML architecture strategy, working with senior Data & AI leadership to align technology stack, operating models, governance frameworks, best practices, and policies with enterprise objectives. 

• ​Establish AI architectural standards and reference architectures that ensure consistency, scalability, and security across all AI/ML initiatives.  

• ​Establish and maintain AI governance processes and architectural guardrails, including guidelines for responsible AI use, model validation, risk management, and regulatory compliance, to ensure safe and ethical use of AI technologies in line with Jazz’s standards. 

• ​Drive adoption of responsible AI practices (e.g. explainability, bias mitigation, data privacy protection) across the organization.  

• ​Provide architectural oversight and technical guidance on AI/ML solution design and integration. Define overarching Enterprise AI architecture standards. 

• ​Conduct architectural reviews and advise project teams on end-to-end AI/ML solution design – from data ingestion and model development to deployment, monitoring, and MLOps – ensuring solutions are robust, scalable, and aligned with enterprise data, security, and compliance standards. 

• ​Serve as a subject-matter expert (SME) and trusted advisor on AI/ML architecture across technology teams.  

• ​Evaluate and design AI/ML and Generative AI solutions, ensuring proposed approaches meet enterprise architectural standards and align with strategic goals. Challenge technical designs and assumptions to uphold Jazz’s requirements for reliable, compliant, and maintainable AI solutions. 

• ​Contribute to discussions on AI use case priority using expertise in AI architecture to advise on feasibility, scalability, reliability, and how to avoid technical debt. 

• ​Vet AI vendors and platforms in collaboration with our AI Development Lead, as well as Infrastructure and Digital Security teams. Together, conduct technical due diligence on solution feasibility, data handling, security, transparency, and overall operational maturity. During such evaluations, this role will focus on the proposed architecture viability and quality. 

• ​Build credibility quickly with senior technical and business stakeholders through effective communication and deep expertise.  

• ​Communicate complex architecture decisions and technical concepts in clear, business-relevant terms, and influence decision-making in cross-functional governance forums to align AI efforts with business objectives.   

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​Required Knowledge, Skills, and Abilities  

• ​Strong communication skills with clear ability to communicate complex AI concepts to business leaders at all levels. 

• ​Familiarity with AI governance and risk frameworks. 

• ​Deep knowledge of AI/ML system architecture, including data pipelines, model development and deployment patterns, cloud-based ML infrastructure, and integration of AI solutions into enterprise systems.  

• ​Proficiency with MLOps concepts, tools, and best practices, such as CI/CD, model versioning, experiment tracking, and monitoring (enabling reliable ML production deployments).  

• ​Strong understanding of core machine learning algorithms and statistical modeling techniques, model evaluation, and real-world deployment considerations.  

• ​Proficiency in one or more programming languages (e.g. Python, R) and ML frameworks (such as TensorFlow, PyTorch, scikit-learn). 

• ​Strong understanding of modern Generative AI concepts and approaches: LLMs, RAG, fine-tuning, agentic patterns, evaluation, and guardrails. 

• ​Significant hands-on experience with at least one major cloud platform (AWS, GCP, or Azure); AWS or GCP preferred. 

• ​Familiarity with AI governance and risk frameworks (e.g., model risk management, responsible AI principles, emerging AI regulation). 

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​Required/Preferred Education and Licenses 

• ​Bachelor’s degree or equivalent practical experience in a quantitative or technical field required.  Advanced degree or equivalent preferred. 

• ​5–7 years of relevant experience in AI, machine learning, or data science required, 10+ years preferred. 

• ​Experience in pharmaceutical, life sciences, or another regulated industry strongly preferred. 

• ​Experience evaluating and integrating third-party AI/ML solutions and vendor platforms strongly preferred. 

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