Jobs · Science

Senior Director, AI Platform Architecture

Thermo Fisher Scientific · Illinois, United States · 1 mo ago
RemoteRemoteScience$168k–$278k/yrFull-time

About the role

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.

Responsibilities

  • 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
  • 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)
    • Establish standards for performance, scalability, reliability, and cost efficiency
    • 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
      • Provides self-service platform capabilities to AI Engineering; ensures adoption through ease-of-use and standardization
      • Enables a federated AI model, allowing domain/product teams to build AI capabilities while leveraging centralized platform standards
      • Act as a central enablement layer supporting both AAI and product-aligned engineering teams
      • Cross-Functional Integration
      • Partner closely with:
        • AI (Applied AI) for solution design and AI architecture
        • Data Platforms for data ingestion, quality, and readiness
        • 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

      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

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