AI & Data Architect

• Define enterprise-wide:
• Data quality
• Major data platforms

We’re hiring at Pitney Bowes, where top talent builds meaningful careers and lasting impact. We Move fast, Deliver excellence, and Win together…that’s The Pitney Bowes way. Here, how we work matters just as much as what we achieve.

• Act with urgency, accountability, and purpose
• Deliver high quality work with consistency and pride
• Collaborate effectively and elevate those around them
• Focus on outcomes that drive impact and growth

The AI & Data Architect is the senior technical leader responsible for defining and executing the enterprise AI and data architecture strategy
. This role establishes a scalable, secure, and governed foundation for data and AI, enabling the organization to deliver measurable business outcomes through advanced analytics, machine learning, and generative AI.

The role acts as the design authority for AI and data platforms—ensuring alignment across business priorities, technology architecture, data governance, and AI capabilities—while driving consistency, reuse, and speed of delivery across the enterprise.

• Enterprise AI & Data Strategy
• Define and own the enterprise AI and data architecture roadmap
• Align AI and data initiatives with business strategy and value realization
• Establish standards for scalable, reusable AI and data capabilities
• Serve as a trusted advisor to CIO and business leadership on AI strategy
• Data Architecture & Platform Leadership
• Design and implement a modern enterprise data architecture (lakehouse / mesh / hybrid models)
• Define enterprise-wide:
  • Data models and canonical schemas
  • Metadata, lineage, and data catalog strategy
  • Data integration and interoperability patterns
• Lead the development of a centralized, scalable data platform
• AI Platform & Engineering Enablement
  • Establish enterprise AI/ML platform capabilities (MLOps / LLMOps)
  • Enable consistent model lifecycle management:
    • Data ingestion → model training → deployment → monitoring
  • Standardize tooling, frameworks, and infrastructure for AI delivery
  • Drive adoption of production-grade AI patterns vs. experimental silos
• Data Governance, Quality & Ownership
  • Define and enforce data governance framework beyond regulatory minimums
  • Clarify data ownership, stewardship, and accountability models
  • Establish enterprise standards for:
    • Data quality
    • Master data management
    • Data lifecycle management
  • Resolve fragmentation and enable a single, trusted data foundation
• Responsible AI & Risk Management
  • Embed responsible AI practices (transparency, fairness, explainability)
  • Ensure alignment with regulatory and internal policy requirements
  • Partner with security and risk leaders to:
    • Mitigate AI-related risks
    • Protect sensitive data and models
    • Establish security standards for data and AI
  • Establish auditability and controls for AI systems
• Architecture Governance & Standards
  • Serve as the enterprise authority for AI and data architecture decisions
  • Define reference architectures, patterns, and reusable components
  • Lead architecture reviews for:
    • Major data platforms
    • AI-enabled applications
  • Ensure consistency across business units and technology teams
• Cross-Functional Leadership & Influence
  • Partner with Engineering, Product, Security, and Operations teams
  • Enable federated adoption model (central platform, distributed execution)
  • Build and mentor a high-performing team of architects and engineers
  • Drive collaboration through AI councils, governance forums, and working groups

• 15+ years in enterprise architecture, data architecture, or AI/ML platforms
• Proven experience building enterprise-scale data and AI platforms
• Experience driving AI adoption from concept to production at scale
• Strong background in cloud platforms (AWS, Azure, GCP) and distributed systems