Senior Director, AI Platform Architecture

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.

About the Position

Reporting to the VP, Head of Analytics and AI, the Senior Director, AI Platform Architecture is a senior leadership role within CRG Digital responsible for defining, building, and scaling the foundational AI platform that enables the rapid development, deployment, and operation of AI‑enabled products and solutions across CRG. This leader owns the end‑to‑end AI platform strategy, architecture, and delivery model—ensuring that AI capabilities are scalable, reusable, secure, and production‑ready. Operating at the intersection of Applied AI (AAI), Data Platforms, and Digital Engineering, this role serves as the backbone of CRG’s AI ecosystem—providing the tools, infrastructure, standards, and services required to accelerate AI innovation while maintaining governance, compliance, and operational excellence. The Director will enable both centralized and federated AI execution, empowering product and engineering teams to build AI solutions efficiently and consistently.

Key Responsibilities

• AI Platform Strategy & Ownership: Define and execute the AI platform strategy and roadmap, aligned to CRG Digital and AI priorities; establish the AI platform as a shared capability layer supporting all AI‑enabled products and workflows; ensure alignment with enterprise architecture, data platform (MDP), and security strategies; drive a platform‑first approach to AI development, enabling reuse and scalability across domains.
• Platform Architecture & Engineering: Lead the design and development of the AI platform architecture, including model development, training, and deployment frameworks; MLOps and LLMOps pipelines; model serving, monitoring, and lifecycle management; integration with data platforms (e.g., Snowflake, Databricks); ensure platform supports GenAI, agentic workflows, and traditional ML use cases; establish standards for performance, scalability, reliability, and cost efficiency.
• Reusable AI Capabilities & Tooling: Build and scale reusable AI components, including model libraries and templates; prompt frameworks and orchestration tools; workflow automation and agent frameworks; enable rapid development through self‑service tools and developer enablement; reduce duplication and accelerate time‑to‑market through standardization and reuse.
• MLOps, Governance & Responsible AI Enablement: Establish and operationalize AI lifecycle management practices, including model versioning, validation, deployment, and monitoring; performance tracking and drift detection; partner with AI Risk/Governance teams to embed compliance, security, and responsible AI principles into the platform; ensure auditability, traceability, and adherence to regulatory and enterprise standards.
• Federated AI Enablement: Provide self‑service platform capabilities to AI Engineering; ensure adoption through ease‑of‑use and standardization; enable a federated AI model, allowing domain/product teams to build AI capabilities while leveraging centralized platform standards; provide tooling, frameworks, and guardrails to ensure consistency and quality across distributed teams; act as a central enablement layer supporting both AAI and product‑aligned engineering teams.
• Cross‑Functional Integration: Partner closely with AAI (Applied AI) for solution design and AI architecture, Data Platforms for data ingestion, quality, and readiness, and Digital Engineering for product integration and delivery; ensure seamless integration of platform capabilities into AI products and workflows.
• Partner & Ecosystem Management: Define and manage relationships with technology vendors and platform partners (e.g., cloud, AI tooling providers); evaluate and integrate emerging AI technologies and tools into the platform ecosystem; optimize the balance between build vs. buy vs. partner decisions.
• Team Leadership & Capability Building: Lead a high‑performing team of AI platform engineers, MLOps specialists, and platform architects; define skills, roles, and career paths for AI platform talent; drive capability building in AI engineering, platform operations, and emerging AI technologies; foster a culture of innovation, reliability, 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
• Increased reuse of AI components and platform capabilities
• Strong performance of AI systems (reliability, scalability, cost efficiency)
• Effective implementation of AI governance and lifecycle management practices
• Development of a scalable and high‑performing AI platform organization

Qualifications

• Bachelor’s degree required; advanced degree preferred (computer science, engineering, AI/ML, or related field)
• 12 years of experience in software engineering, data platforms, AI/ML engineering, or platform leadership roles
• Proven track record of building and scaling AI/ML platforms or data platforms in enterprise environments
• Strong understanding of AI/ML and GenAI technologies, MLOps/LLMOps practices and Cloud platforms and modern data architectures
• Experience operating in complex, matrixed organizations with cross‑functional stakeholders
• Experience in regulated environments (healthcare/life sciences) preferred

Knowledge, Skills, and Abilities

• Deep technical and strategic understanding of AI platform architecture and operations
• Strong systems thinking with ability to balance innovation, scalability, and governance
• Ability to translate platform capabilities into business and product impact
• Strong leadership and team‑building capabilities
• Excellent stakeholder management and ability to influence across Product, Data, AI, and Engineering teams
• Ability to operate in a fast‑paced, evolving technology landscape

Pay

The salary range estimated for this position based in North Carolina is $167,500.00–$278,000.00. This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy.

Benefits

• A choice of national medical and dental plans, and a national vision plan, including health incentive programs
• Employee assistance and family support programs, including commuter benefits and tuition reimbursement
• At least 120 hours paid time off (PTO), 10 paid holidays annually, paid parental leave (3 weeks for bonding and 8 weeks for caregiver leave), accident and life insurance, and short‑ and long‑term disability in accordance with company policy
• Retirement and savings programs, such as our competitive 401(k) U.S. retirement savings plan
• Employees’ Stock Purchase Plan (ESPP) offers eligible colleagues the opportunity to purchase company stock at a discount

Schedule

Work Schedule Standard (Mon‑Fri); Environmental Conditions Office