Senior Director, AI Enterprise Architecture

Introduction to role:

Are you ready to design the enterprise AI backbone that powers faster science and smarter operations? In this senior leadership role, you will define and scale platform and enabling architectures that turn agentic AI, foundation models, and interoperable context management into measurable outcomes—accelerating decisions in R&D and across the enterprise while meeting the highest standards of data governance and regulatory compliance.

You will develop the roadmap and reference architectures applicable to open-source and also proprietary ecosystems. You will collaborate with teams using Amazon Q, Amazon Bedrock, SageMaker, OpenAI, Databricks, and other new technologies. Your work will connect the dots across domains, simplify a complex landscape, and increase the agility of critical business processes—driving efficiencies, reducing risk, and unlocking value at scale.

Can you translate brand new AI into secure, scalable platforms that speed delivery of life-changing medicines while improving the way we operate every day? If you thrive on uniting diverse experts to tackle complex challenges and make decisive progress, this is your opportunity to lead.

Accountabilities:

• Enterprise AI Strategy: Define and evolve the organizational AI architecture strategy aligned with scientific and business objectives, ensuring platforms and products deliver tangible value.
• Agentic AI and Large-scale Models: Lead architecture build for multi-agent systems and foundational AI models (LLMs, multimodal), enabling resilient, observable, and governable solutions.
• Reference Frameworks and Protocols: Establish reusable blueprints and standards for platforms across both open-access and licensed environments to accelerate safe adoption and scale.
• Interoperability via MCP: Drive adoption of the Model Context Protocol for consistent context management and interoperability across tools and services.
• Scalable Platforms on Cloud and Mixed Environments: Develop cloud and hybrid environment AI platforms with AWS, OpenAI, Databricks, and related services to improve enterprise throughput, reliability, and cost efficiency.
• AI Lifecycle Enablement: Enable the full AI lifecycle—from discovery and MVP to productionization, optimization, and retirement—backed by clear SLOs and feedback loops.
• Advanced AI Capabilities: Design and guide implementation of RAG patterns, vector databases, knowledge systems, and the data pipelines they depend on.
• Responsible and Secure AI: Embed governance, compliance, and risk management, anticipating threats such as data poisoning, model theft, and adversarial attacks, and translating regulations into actionable controls.
• Cross-Enterprise Alignment: Partner with product, data governance, security, engineering, and business leaders to align AI initiatives and accelerate high-value use cases.
• Leadership and Mentorship: Build and mentor high-performing enterprise and solution architecture teams, developing skills, career paths, and delivery excellence.
• Value Acceleration: Identify, assess, and prioritize use cases with business stakeholders; translate strategy into practical solutions and constructively challenge low-value or misaligned initiatives.
• Business-Driven Delivery: Gather insights from users, data scientists, engineers, and operations to align delivery with current and future needs, turning them into scalable, reliable processes.
• Technology Selection and Integration: Select fit-for-purpose technologies across open-source and commercial platforms, recommending cloud, on-premises, or hybrid deployment models and ensuring seamless integration with data and analytics ecosystems.
• Continuous Improvement and MLOps: Evaluate tools and practices across data, models, and software engineering; set up feedback mechanisms for service performance, model recalibration, and retraining.
• ML/AI Pipelines: Guide pipeline architecture decisions across data management, governance, model development, deployment, and production operations, with clear trade-off reasoning.
• Modern Engineering: Apply strong software engineering and DevOps principles, including Git, containers, Kubernetes, and CI/CD, to increase speed and reliability.
• Applied Data Science Understanding: Work fluently with analytics and ML concepts and tooling (e.g., SAS, R, Python, TensorFlow, ensembles, neural networks) to bridge architecture and data science practices.
• Executive Thought Leadership: Act as a change agent and trusted advisor; communicate opportunities, limitations, and risks of AI to senior stakeholders and influence decision-making.
• Enterprise Collaboration: Build strong partnerships across data science, engineering, architecture, and executive leadership to align around shared outcomes.
• Information Architecture Ownership: Del