About this role
The AI & Data Architect is the senior technical leader and design authority for Pitney Bowes’ enterprise AI and data architecture. The role defines a scalable, secure and governed foundation for advanced analytics, machine learning and generative AI, aligning platform decisions with business priorities and measurable outcomes.
Responsibilities
- Own the enterprise AI and data architecture roadmap, establish reusable standards, and advise the CIO and business leaders on AI strategy.
- Design modern data architecture using lakehouse, data mesh or hybrid models. Define enterprise data models, canonical schemas, metadata, lineage, catalog, integration and interoperability approaches, and lead development of a centralized, scalable data platform.
- Establish AI/ML platform capabilities, including MLOps and LLMOps. Standardize tooling and the lifecycle from data ingestion and model training through deployment and monitoring, emphasizing production-grade delivery.
- Define governance for data ownership, stewardship, quality, master data and lifecycle management. Embed responsible AI practices, including transparency, fairness and explainability; partner with security and risk teams on sensitive-data protection, model security, auditability and compliance.
- Set reference architectures and reusable components, lead reviews of major data platforms and AI-enabled applications, and coordinate a federated model with Engineering, Product, Security and Operations. Build and mentor a team of architects and engineers.
Requirements
- 15+ years in enterprise architecture, data architecture or AI/ML platforms; proven experience building enterprise-scale data and AI platforms and moving AI initiatives from concept to production at scale.
- Strong background in cloud platforms (AWS, Azure, GCP) and distributed systems. Technical expertise includes lakehouse and data mesh architectures, ETL/ELT, streaming pipelines, model lifecycle management, MLOps, generative AI, LLM integration, metadata, lineage, APIs, microservices and cloud-native architecture. Experience establishing governance, influencing executives and leading teams is required.
Success over 12–24 months includes platform adoption across business units, reduced data fragmentation, standardized AI delivery, stronger governance and measurable improvements in business impact, data quality, deployment speed, model performance and risk. Listed locations include Shelton, Connecticut, and remote options in Florida, Ohio, Virginia and North Carolina; the page indicates seven locations but names only five. Applicants must be authorized to work in the US; the employer will not sponsor employment visas now or in the future. Comprehensive benefits and career-development opportunities are offered.