Executive Director, Applied AI

Job description

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description About the Team: CRG Digital AI is the engine that translates our digital strategy into scalable, production-ready AI capabilities that drive measurable business impact. Operating in close partnership with Product, Data, and Engineering, the team embeds AI across our digital portfolio to accelerate clinical trial execution, enhance data-driven decision-making, and unlock differentiated value for our customers. Through a combination of centralized platforms, standards, and federated execution, CRG Digital AI enables rapid innovation while ensuring consistency, quality, and responsible AI practices. This position is a band 10 role, remotely based in the US Position Overview: Reporting into the VP, Head of Analytics and AI, the Executive Director, Applied AI is a senior leadership role responsible for building, operating, and scaling CRG Digital’s end-to-end AI engineering and platform capability, anchored in reusable architecture and scalable execution systems. This includes ownership of AI engineering delivery, platform architecture, and the integrated automation layer—ensuring that AI-enabled solutions are production-ready, scalable, and seamlessly embedded into business operations. Operating at the intersection of Applied AI (AAI), Data, and Product Engineering, this leader unifies solution delivery and platform enablement into a cohesive, high-performing system. The role is accountable for both what gets built and how it runs, combining product-aligned engineering teams with a robust, reusable platform that accelerates development, enforces standards, and enables federated AI adoption across CRG. A core part of this mandate is defining and scaling reusable AI architecture patterns, services, and components that support rapid development of AI-enabled capabilities across domains. A strong focus of this role is the development of a modern AI-native execution layer, where AI-driven decisioning, services, and workflows are operationalized through APIs, orchestration frameworks, and automation capabilities. Traditional RPA is evolved and integrated into this broader architecture as one of several execution mechanisms, rather than a standalone capability, ensuring consistency, scalability, and alignment with AI-first design principles. This role plays a crucial part in shaping the next generation of role-based, AI-enabled operations, where AI capabilities are embedded directly into how work is performed. The platform and engineering organization will define the architectural foundation for these operating models, enabling reusable patterns for human–AI interaction, decision support, and autonomous or semi-autonomous execution. The Executive Director, Applied AI partners closely with: Solution Architecture to translate AI use cases into scalable technical solutions AI Value Realization & Enablement (AVRE) to ensure solutions are designed for real-world workflow integration AI Risk & Compliance to embed governance and responsible AI practices into platform and engineering systems Data and Product teams to align on priorities, architecture, and delivery outcomes This role is critical to enabling CRG’s AI strategy by delivering a high-throughput, platform-enabled engineering organization that balances speed, quality, cost efficiency, and regulatory compliance—while laying the architectural and engineering foundation for next-generation AI, agentic systems, and role-based operating models. Key Responsibilities AI Engineering Delivery & Product Integration Lead product-aligned engineering teams to deliver AI-enabled applications and services at scale, with a strong emphasis on AI-native development practices Redefine engineering productivity by driving adoption of AI-assisted and agent-based development, including AI coding assistants (e.g., Codex-style tools), agent-enabled code generation, testing, and refactoring and automated documentation and code review workflows. Establish a target operating model where individual engineers are significantly amplified by AI tooling, enabling 1 engineer to deliver the output of multiple traditional engineers through effective human–AI collaboration. Shift engineering focus from manual coding to solution architecture and system design, validation, testing, and quality assurance of AI-generated code and integration of AI services into scalable systems. Own the reliable, high-quality delivery of AI/ML and GenAI solutions, AI-enabled product features and APIs, integrated data and feature pipelines. Establish a high-throughput engineering model driven by rapid iteration cycles, automation-first development workflows and reuse of components and services. Partner with Solution Architecture to translate use cases into scalable, production-ready solutions, ensuring alignment between design intent and engineering execution. Ensure seamless integration of AI capabilities into digital products and workflows, with a focus on speed, adaptability, and maintainability. AI-Native Execution Layer (Automation & Orchestration) Build and scale a modern AI-native execution layer that operationalizes AI-driven decisions into real-world actions Integrate and evolve capabilities including APIs and system integrations, workflow orchestration frameworks and intelligent automation (including RPA as a supporting capability). Ensure automation is AI-driven, not task-driven, reusable and standardized and tightly integrated with platform and AI services. Enable execution patterns that support human-in-the-loop, semi-autonomous, and agentic workflows. MLOps, Lifecycle Management & Operational Excellence Establish and scale end-to-end AI lifecycle management, including model development, validation, deployment, and monitoring, versioning, performance tracking, and drift detection. Ensure platform and engineering systems meet requirements for reliability and scalability, cost efficiency, observability and monitoring. Embed governance-by-design in partnership with AI Risk & Compliance, including auditability and traceability secure and compliant development practices. Partner Strategy & Capability Scaling Define and manage the ecosystem of engineering and platform partners. Drive effective onshore/offshore and partner delivery models aligned to platform and engineering needs. Ensure partners contribute to reusable assets, platform capabilities and speed/quality of delivery. Lead internal capability building in AI engineering, platform engineering and emerging AI and agentic technologies. Talent & Organizational Leadership Build and lead a high-performing organization across AI engineering, platform engineering and automation and orchestration capabilities Define roles, skill models, and career paths aligned to future-state AI capabilities Foster a culture of engineering excellence, innovation and reuse and accountability and continuous improvement Measures of Success: Adoption and utilization of the AI platform across CRG Digital teams Reduction in time-to-deploy AI solutions and increased development velocity Education and Experience: Bachelor’s degree required; advanced degree preferred (computer science, engineering, AI/ML, or related field) 15+ years of experience in software engineering, platform engineering, or technology leadership roles, with a proven track record of building and scaling high-performing engineering organizations Demonstrated experience defining and implementing scalable, reusable platform architectures and shared capability layers in complex enterprise environments Experience delivering AI/ML and/or GenAI-enabled systems in production, including understanding of model lifecycle, integration patterns, and operational considerations Proven ability to evolve engineering organizations toward modern, automation-first and AI-assisted development practices, driving m