AI & Data Architect

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must only include sections that have real specific content. The posting includes: Job Description (which is basically responsibilities). There's "You Bring" (requirements/qualifications/skills). There's "Success Outcomes (12–24 Months)" maybe not a standard heading; could be considered responsibilities? There's "Key Performance Indicators (KPIs)" maybe not needed. There's "Sponsorship" (visa sponsorship) - maybe not a standard heading; could be part of Requirements? There's "We Will Provide" (benefits). Also "Offer comprehensive benefits globally". So we can have Benefits section. We need to avoid repeating job title as heading. No Job ID. Remove boilerplate: EEO, equal opportunity, scam warning, application instructions, etc. Also remove "We’re hiring at Pitney Bowes..." maybe that's intro fluff; but we need to preserve substantive information. The intro includes culture statements; maybe we can put under About the role? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have About the role (intro), Responsibilities (Job Description), Requirements/Qualifications/Skills (You Bring), Benefits (We Will Provide). Possibly also include "Success Outcomes" as part of Responsibilities? Could be separate but not standard heading; better to incorporate into Responsibilities. We must not create Pay/Schedule unless present. Not present. We must not emit heading with filler text. So only include sections with content. We need to use

• for any list of duties/requirements/benefits, and

  for prose. We need to preserve all substantive information; do not invent or summarize away detail. Thus we need to extract bullet points from responsibilities and requirements. Let's parse. Job Description section includes many paragraphs; we can turn each bullet-like sentence into

• . But we need to decide grouping. The text: "Job Description 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. You Will Enterprise AI & Data StrategyDefine and own the enterprise AI and data architecture roadmapAlign AI and data initiatives with business strategy and value realizationEstablish standards for scalable, reusable AI and data capabilitiesServe as a trusted advisor to CIO and business leadership on AI strategy Data Architecture & Platform LeadershipDesign and implement a modern enterprise data architecture (lakehouse / mesh / hybrid models)Define enterprise-wide:Data models and canonical schemasMetadata, lineage, and data catalog strategyData integration and interoperability patternsLead the development of a centralized, scalable data platform AI Platform & Engineering EnablementEstablish enterprise AI/ML platform capabilities (MLOps / LLMOps)Enable consistent model lifecycle management:Data ingestion → model training → deployment → monitoringStandardize tooling, frameworks, and infrastructure for AI deliveryDrive adoption of production-grade AI patterns vs. experimental silos Data Governance, Quality & OwnershipDefine and enforce data governance framework beyond regulatory minimumsClarify data ownership, stewardship, and accountability modelsEstablish enterprise standards for:Data qualityMaster data managementData lifecycle managementResolve fragmentation and enable a single, trusted data foundation Responsible AI & Risk ManagementEmbed responsible AI practices (transparency, fairness, explainability)Ensure alignment with regulatory and internal policy requirementsPartner with security and risk leaders to:Mitigate AI-related risksProtect sensitive data and modelsEstablish security standards for data and AIEstablish auditability and controls for AI systems Architecture Governance & StandardsServe as the enterprise authority for AI and data architecture decisionsDefine reference architectures, patterns, and reusable componentsLead architecture reviews for:Major data platformsAI-enabled applicationsEnsure consistency across business units and technology teams Cross-Functional Leadership & InfluencePartner with Engineering, Product, Security, and Operations teamsEnable federated adoption model (central platform, distributed execution)Build and mentor a high-performing team of architects and engineersDrive collaboration through AI councils, governance forums, and working groups" Thus we can break into bullet points under Responsibilities. Similarly "You Bring" section: "You Bring 15+ years in enterprise architecture, data architecture, or AI/ML platformsProven experience building enterprise-scale data and AI platformsExperience driving AI adoption from concept to production at scaleStrong background in cloud platforms (AWS, Azure, GCP) and distributed systems Technical Expertise Data architecture: lakehouse, data mesh, ETL/ELT, streaming pipelinesAI/ML: model lifecycle, MLOps, generative AI, LLM integrationData governance: metadata, lineage, quality frameworksPlatform engineering: APIs, microservices, cloud-native architecturesSecurity and compliance principles for data and AI systems Leadership & Operating Model Ability to operate at both strategic and deep technical levelsStrong experience establishing enterprise standards and governanceProven ability to influence executive stakeholders and cross-functional teamsTrack record of building high-talent-density teams" We can split into Requirements (experience) and Skills (technical expertise). But we can combine into one Requirements section with bullet points. We also have "Success Outcomes (12–24 Months)" and "Key Performance Indicators (KPIs)" maybe we can incorporate into Responsibilities as expected outcomes. But we should not create extra sections unless they have real specific content. The Success Outcomes are specific; we could add a section "Expected Outcomes" but not in allowed headings list. The allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we should map content to those. Thus: - About the role: intro paragraph about Pitney Bowes culture and role overview (first paragraph before "Job Description"). - Responsibilities: bullet list of duties from "You Will" sections. - Requirements: bullet list of experience requirements from "You Bring" (years, experience building platforms, driving AI adoption, cloud background). - Skills: bullet list of technical expertise and leadership skills. - Benefits: bullet list from "We Will Provide the opportunity to grow and develop your career Offer an inclusive environment that encourages diverse perspectives and ideas Deliver challenging and unique opportunities to contribute to the success of a transforming organization Offer comprehensive benefits globally (PB Benefits and Wellbeing Programs)". Also maybe mention "Opportunity to grow and develop your career", "inclusive environment", "challenging opportunities", "comprehensive benefits globally". We must not include boilerplate like EEO, visa sponsorship, etc. The Sponsorship section says "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)." That's a requirement but it's about sponsorship; we could include under Requirements as a bullet: "Must be legally authorized to work in the US; no visa sponsorship." However the instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Sponsorship is not exactly boilerplate; it's a requirement about work authorization. Might be considered legal? It's about visa sponsorship. Might be okay to keep as requirement. But we need to decide. The instruction says remove legal/EEO/equal-opportunity/scam-warning boilerplate. Sponsorship is about work authorization, not EEO. I'd keep it as a requirement. Now we need to output only HTML fragment, no /, no markdown. We'll produce something like:

  About the role

  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

  But we need to preserve the content exactly? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. So we need to keep the sentences as they are, but we can format them into

  . The intro includes some weird formatting: "We Move fast, Deliver excellence, and Win together…that’s The Pitney Bowes way." We'll keep that. Now Responsibilities: we need

  • for each duty. Let's extract bullet points from "You Will" sections: - 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 - 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 - 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 - 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 - 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 - 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 - 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 We could also include the Success Outcomes as bullet points under Responsibilities or maybe separate but we can incorporate. Success Outcomes (12–24 Months): - Enterprise AI and data platform established and adopted across business units - Data fragmentation reduced; clear ownership and governance in place - AI delivery lifecycle standardized with measurable improvements in speed and quality - Increased business impact from AI (revenue, cost efficiency, decision quality) - Strong architecture governance model driving consistency and reuse We could add these as responsibilities (expected outcomes). It's okay. Now Requirements: bullet points from "You Bring" experience: - 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 - Must be legally authorized to work in the US; no visa sponsorship for employment visa status now or in the future (e.g., H-1B) Now Skills: bullet points from technical expertise and leadership: Technical Expertise: - Data architecture: lakehouse, data mesh, ETL/ELT, streaming pipelines - AI/ML: model lifecycle, MLOps, generative AI, LLM integration - Data governance: metadata, lineage, quality frameworks - Platform engineering: APIs, microservices, cloud-native