AI & Data Architect

About the role

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.

Responsibilities

• 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; lead development of metadata, lineage, and data catalog strategy; lead data integration and interoperability patterns; lead 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, and 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, and 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 and 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.

Requirements

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

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 architectures.
• Security and compliance principles for data and AI systems.

Leadership & Operating Model

• 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.

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.

Key Performance Indicators (KPIs)

• Business Impact: AI-driven revenue contribution and cost optimization; adoption of data and AI capabilities across business units.
• Platform & Delivery: Time-to-deploy AI models; platform adoption rate (% of workloads on standardized platform).
• Data Quality & Governance: % of critical data assets with defined ownership; data quality score improvements.
• AI Effectiveness: Model performance (accuracy, drift, business outcome metrics); AI project ROI.
• Risk & Compliance: % of AI systems under governance; reduction in data and AI-related risk incidents.

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).