AI & Data Architect

About the role

This senior technical leadership role defines and executes the enterprise AI and data architecture strategy, establishing a scalable, secure, and governed foundation for data and AI that delivers measurable business outcomes through advanced analytics, machine learning, and generative AI.

Responsibilities

• Enterprise AI & Data Strategy: Define and own the enterprise AI and data architecture roadmap; align 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/catalog strategy, and data integration/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 frameworks 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 to 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/models, establish security standards, and ensure 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 a 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.