Principal Architect - AI

Sedgwick•Telecommuter FL, FL

About The Position

The AI Principal Architecture within Sedgwick’s Transformation Office serves as the enterprise executive accountable for AI architecture strategy, governance, and platform direction. The role drives business transformation through Generative AI, Agentic AI, and advanced intelligence capabilities, establishing an enterprise intelligence layer that supports secure, ethical, and scalable AI adoption across Lines of Business, shared services, and the technology ecosystem. Operating across enterprise architecture, product and platform strategy, risk governance, and execution oversight, this leader defines the architectural vision and operating model for LLM-powered platforms, agentic systems, and conversational and automation capabilities. The role influences enterprise priorities and investments, sets cross-functional standards, and integrates AI with legacy and modern environments across AWS and Azure to deliver measurable business value and operational outcomes. The position owns enterprise AI and agentic AI architecture strategy, governance, platforms, RAG and knowledge architecture, integration patterns, and AI-ready data foundations. It also serves as an executive technical authority on responsible AI and risk, leads AI architecture portfolio prioritization and value realization, and supports the transition of AI initiatives into production operations. The role develops architecture talent, influences vendor and tooling strategy, represents the organization in executive engagements, and travels as required.

Requirements

Master’s degree in AI, Machine Learning, Computer Science, Engineering, or a quantitative discipline from an accredited college or university is preferred.
Ten or more years of related, progressive experience in enterprise architecture, platform architecture, or large-scale solution architecture, with seven or more years leading delivery and/or architecture of enterprise-scale Generative AI, RAG, and Agentic AI capabilities in complex corporate environments, or an equivalent combination of education and experience.
Executive-level expertise with AWS AgentCore and Azure AI Agent Service.
Deep mastery of agentic frameworks such as LangGraph and CrewAI.
Experience establishing enterprise AI governance, driving cross-Line-of-Business adoption, and operationalizing AI solutions within security, risk, and compliance constraints.
Ability to drive alignment and decision-making with executive stakeholders across Technology, Security, Risk, Legal/Compliance, and Lines of Business.
Expert ability to bridge Data Science, AI Engineering, and Enterprise Architecture, ensuring solutions are statistically sound, governed, scalable, and aligned to business outcomes.
Advanced mastery of Python, SQL, and PySpark.
Deep expertise in vector stores such as Pinecone, OpenSearch, and Azure AI Search, LLM platforms, and AI/ML lifecycle management.
Proven leadership in AWS and Azure enterprise environments.
Ability to translate architecture decisions into measurable outcomes, cost efficiency, operational resilience, and risk reduction.
Ability to work in a team environment.
Ability to meet or exceed Performance Competencies.

Nice To Haves

Strong functional exposure to advanced analytics platforms such as Palantir Foundry/AIP.
Demonstrated success operating within a Transformation Office or enterprise innovation function, establishing operating models, reusable patterns, and governance to scale adoption responsibly.

Responsibilities

Own and set the enterprise Generative AI and Agentic AI architecture vision and multi-year roadmap, aligned with enterprise strategy, transformation priorities, operating model, and risk posture.
Chair or co-lead executive architecture and AI governance forums, establishing decision rights, design authorities, exception processes, and enterprise adoption guardrails.
Direct the enterprise intelligence-layer strategy, including reference architectures, standards, reusable patterns, and platform capabilities.
Define and govern the enterprise agentic AI strategy and platform blueprint, including the design, deployment, and lifecycle management of autonomous and semi-autonomous agents.
Establish enterprise patterns for multi-agent orchestration, reasoning, tool use, memory, human-in-the-loop controls, observability, fail-safes, and model and agent lifecycle management.
Set standards for Generative AI and agentic solutions across AWS and Azure, addressing reliability, resilience, performance, security, cost optimization, and regulatory compliance.
Own the enterprise RAG strategy and common frameworks, including best practices for retrieval, embeddings, vector stores, context management, evaluation, and performance tuning.
Establish knowledge governance standards for content provenance, permissions and access, lineage, data quality, and approved knowledge sources.
Define integration patterns for connecting AI solutions with legacy platforms and modern cloud services while balancing modernization, resiliency, and operational continuity.
Partner with Enterprise Architecture, Infrastructure, and Platform teams to embed the AI ecosystem in enterprise identity, networking, monitoring, disaster recovery, and service management.
Partner with data, analytics, and platform teams to define shared data architectures, ontologies, governance models, and operating practices that support AI, agentic workflows, and advanced analytics.
Establish standards for AI-ready data products, metadata, semantic layers, and measurable data quality thresholds tied to AI performance and outcomes.
Serve as the executive technical authority for security, risk, legal, compliance, and audit stakeholders, ensuring alignment with model risk, privacy, security, regulatory requirements, and Responsible AI principles.
Establish practices for model governance, evaluation, red teaming, bias mitigation, explainability, and controls so outputs are reliable, auditable, and aligned with business policy and ethical standards.
Lead the enterprise AI architecture portfolio and prioritize capabilities and investments based on business value, risk, feasibility, and reuse.
Define success metrics and mechanisms for tracking value realization, including productivity, cycle-time reduction, loss cost improvement, customer experience, quality, and compliance outcomes.
Partner with Technology and Business leadership to transition AI initiatives into production operations with clear support, ownership, and continuous improvement mechanisms.
Build and develop enterprise AI architecture talent by coaching senior architects, establishing capability standards, and creating communities of practice across AI engineering and enterprise architecture.
Influence platform and tooling decisions, vendor strategy, and ecosystem partnerships to accelerate innovation while managing cost, risk, and maintainability.
Represent the organization as a senior AI architecture leader in executive engagements, internal stakeholder communications, and cross-enterprise transformation initiatives.
Perform other duties as assigned.
Travel as required.