Principal AI & Data Architect

Pitney Bowes•Shelton, CT

23h•Hybrid

About The Position

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. We’re looking for people who: 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. You Are A senior technical leader who sets the direction for the organization’s AI and data architecture. You build the scalable, secure, and governed foundation that supports analytics, machine learning, and generative AI. You serve as the architecture authority for AI and data platforms and ensure alignment across business priorities, technology strategy, and delivery teams.

Requirements

Enterprise experience with 15 or more years in enterprise architecture, data architecture, or AI and ML platforms.
Proven success building enterprise‑scale data and AI platforms.
Experience driving AI adoption from concept to production at scale.
Strong background in AWS, Azure, GCP, and distributed systems.
Technical depth across lakehouse, data mesh, ETL and ELT, streaming pipelines, model lifecycle management, MLOps, generative AI, LLM integration, metadata, lineage, and cloud‑native architectures.
Understanding of security and compliance requirements for data and AI systems.
Ability to operate at both strategic and hands‑on technical levels.
Experience establishing enterprise standards and governance.
Proven ability to influence senior stakeholders and cross‑functional teams.
Track record of building high‑talent technical teams.
Must be legally authorized to work in the US. Employer will not sponsor position for employment visa status now or in the future (ex. H-1B).

Responsibilities

Lead enterprise AI and data strategy and own the architecture roadmap.
Align AI and data initiatives with business goals and measurable value.
Establish standards for scalable and reusable AI and data capabilities.
Serve as a trusted advisor to technology and business leaders on AI strategy.
Design modern data architecture including lakehouse, mesh, and hybrid models.
Define enterprise data models, canonical schemas, metadata strategy, lineage, and integration patterns.
Lead the development of a centralized and scalable enterprise data platform.
Build AI and ML platform capabilities including MLOps and LLMOps.
Enable consistent model lifecycle management from data ingestion through deployment and monitoring.
Standardize tooling, frameworks, and infrastructure for AI delivery.
Drive adoption of production‑grade AI patterns and reduce experimental silos.
Define and enforce data governance including ownership, stewardship, quality, MDM, and lifecycle management.
Resolve fragmentation and establish a single trusted data foundation.
Embed responsible AI practices including transparency, fairness, and explainability.
Partner with security and risk teams to protect sensitive data and models and mitigate AI‑related risks.
Establish auditability and controls for AI systems.
Lead architecture governance through reference architectures, patterns, and reusable components.
Conduct architecture reviews for major data platforms and AI‑enabled applications.
Partner with engineering, product, security, and operations teams to support a federated adoption model.
Build and mentor a high‑performing team of architects and engineers.
Drive collaboration through councils, governance forums, and working groups.