Principal Architect - AI

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Principal Architect - AI

PRIMARY PURPOSE : The AI Principal Architecture within the Transformation Office serves as the enterprise executive accountable for AI architecture strategy, governance, and platform direction—driving large-scale business transformation through applied Generative AI, Agentic AI, and advanced intelligence capabilities. This role establishes and evolves the enterprise “intelligence layer” as a strategic capability, ensuring AI is delivered securely, ethically, and at scale across the organization’s Lines of Business, shared services, and core technology ecosystem.

Operating at the intersection of enterprise architecture, product/platform strategy, risk governance, and execution oversight, this leader defines the end-to-end architectural vision and operating model for LLM-powered platforms, agentic systems, and conversational/automation capabilities. The role influences enterprise prioritization and investment decisions, sets cross-functional standards, and ensures AI capabilities integrate effectively with legacy and modern environments across AWS and Azure, translating complex AI innovation into measurable business value and operational outcomes.

ESSENTIAL FUNCTIONS AND RESPONSIBILITIES

Enterprise AI Strategy, Governance & Executive Leadership

• •Owns and sets the enterprise Generative AI and Agentic AI architecture vision and multi-year roadmap, aligning to enterprise strategy, transformation priorities, operating model, and risk posture.
• •Chairs or co-leads executive architecture and AI governance forums (e.g., AI Architecture Review Board), establishing decision rights, design authorities, exception processes, and enterprise adoption guardrails.
• •Directs the enterprise “intelligence layer” strategy, defining reference architectures, standards, reusable patterns, and platform capabilities that enable consistent, scalable AI adoption across the enterprise.

Agentic AI & Platform Architecture at Scale

• •Defines and governs the enterprise agentic AI strategy and platform blueprint, including design, deployment, and lifecycle management of autonomous and semi-autonomous agents using platforms such as AWS AgentCore and Azure AI Agent Service.
• •Establishes enterprise patterns for multi-agent orchestration, reasoning, tool-use, memory, human-in-the-loop controls, observability, fail-safes, and model/agent lifecycle management, ensuring scalable and auditable decision workflows.
• •Sets enterprise standards for GenAI and agentic solutions across AWS and Azure, ensuring reliability, resilience, performance, security, cost optimization, and regulatory compliance.

Enterprise RAG, Knowledge Architecture & Reuse

• •Owns the enterprise Retrieval-Augmented Generation (RAG) strategy and common frameworks, defining best practices for retrieval strategies, embeddings, vector stores, context management, evaluation, and performance tuning.
• •Establishes enterprise standards for knowledge governance, including content provenance, permissions and access models, lineage, data quality, and safe/approved knowledge sources.

Integration, Modernization & Cross-Platform Enablement

• •Defines enterprise integration patterns that enable AI solutions to interface with legacy platforms (e.g., mainframes, SQL Server, on-prem systems) and modern cloud services, balancing modernization with resiliency and minimizing operational disruption.
• •Partners with Enterprise Architecture, Infrastructure, and Platform teams to ensure the AI ecosystem is embedded within enterprise identity, networking, monitoring, disaster recovery, and service management capabilities.

Data, Ontologies, and AI-Ready Enterprise Foundations

• •Partners with data, analytics, and platform teams leveraging technologies such as Palantir, Databricks, and Snowflake to define shared enterprise data architectures, ontologies, governance models, and operating practices that enable AI, agentic workflows, and advanced analytics at scale.
• •Establishes standards for AI-ready data products, metadata, semantic layers, and measurable data quality thresholds tied to AI performance and outcomes.

Risk, Compliance, and Responsible AI Executive Accountability

• •Serves as the executive technical authority to security, risk, legal, compliance, and audit stakeholders—ensuring AI solutions align with model risk, privacy, security, regulatory requirements, and Responsible AI principles.
• •Establishes enterprise practices for model governance, evaluation, red teaming, bias mitigation, explainability, and controls, ensuring outputs are reliable, auditable, and aligned to business policy and ethical standards.

Portfolio, Value Realization & Operational Outcomes

• •Leads the AI architecture portfolio across the enterprise, prioritizing capabilities and investments based on business value, risk, feasibility, and reuse.
• •Defines success metrics and mechanisms to track value realization (e.g., productivity, cycle-time reduction, loss cost improvement, customer experience, quality, compliance outcomes).
• •Partners with Technology and Business leadership to ensure AI initiatives transition effectively into production operations with clear support, ownership, and continuous improvement mechanisms.

ADDITIONAL FUNCTIONS and RESPONSIBILITIES

• •Builds and develops enterprise AI architecture talent, including coaching senior architects, establishing capability standards, and creating communities of practice across AI engineering and enterprise architecture.
• •Influences platform/tooling decisions, vendor strategy, and ecosystem partnerships to accelerate innovation while managing cost, risk, and long-term maintainability.
• •Represents the organization as a senior AI architecture leader in executive engagements, internal stakeholder communications, and cross-enterprise transformation initiatives.
• •Performs other duties as assigned.
• •Travel as required.

QUALIFICATIONS

Education & Licensing

Master’s degree in AI, Machine Learning, Computer Science, Engineering, or a quantitative discipline from an accredited college or university preferred.

Experience

Ten (10+) years of related, progressive experience in in enterprise architecture, platform architecture, or large-scale solution architecture, with seven (7+) years leading delivery and/or architecture of enterprise-scale GenAI, RAG, and Agentic AI capabilities in complex corporate environments, or an equivalent combination of education and experience required.

Demonstrated executive-level expertise with AWS AgentCore and Azure AI Agent Service; deep mastery of agentic frameworks (e.g., LangGraph, CrewAI) required.

Proven experience establishing enterprise AI governance, driving cross-LOB adoption, and operationalizing AI solutions within security, risk, and compliance constraints.

Skills & Knowledge

• •Enterprise leadership and influence: Ability to drive alignment and decision-making with executive stakeholders across Technology, Security, Risk, Legal/Compliance, and Lines of Business
• •Architectural authority: Expert ability to bridge Data Science, AI Engineering, and Enterprise Architecture—ensuring solutions are statistically sound, governed, scalable, and aligned to business outcomes
• •Technical depth: Advanced mastery of Python, SQL, and PySpark; deep expertise in vector stores (e.g., Pinecone, OpenSearch, Azure AI Search), LLM platforms, and AI/ML lifecycle management
• •Cloud & platform expertise: Proven leadership in AWS and Azure enterprise env