AI & Data Architect
Indexed description
- 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 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 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
- 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 architectures
- Security and compliance principles for data and AI systems
- Ability to operate at both strategic and deep technical levels
- Strong experience establishing enterprise standards and governance
- Proven ability to influence executive stakeholders and cross-functional teams
- Track record of building high-talent-density teams
- 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
- AI-driven revenue contribution and cost optimization
- Adoption of data and AI capabilities across business units
- Time-to-deploy AI models
- Platform adoption rate (% of workloads on standardized platform)
- % of critical data assets with defined ownership
- Data quality score improvements
- Model performance (accuracy, drift, business outcome metrics)
- AI project ROI
- % of AI systems under governance
- Reduction in data and AI-related risk incidents
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)
All qualified applicants, including Veterans and Individuals with Disabilities, are encouraged to apply.
All interested individuals must apply online. Individuals with disabilities who cannot apply via our online application should refer to the alternate application options via our Individuals with Disabilities link.
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