Immediate Need: Devops Architect

Fortune Best Workplaces in Financial Services & Insurance 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. 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. 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. Leads the AI architecture portfolio across the enterprise, prioritizing capabilities and investments based on business value, risk, feasibility, and reuse. 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. 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. Travel as required. Master’s degree in AI, Machine Learning, Computer Science, Engineering, or a quantitative discipline from an accredited college or university preferred. 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; Proven experience establishing enterprise AI governance, driving cross-LOB 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; Pinecone, OpenSearch, Azure AI Search), LLM platforms, and AI/ML lifecycle management Cloud & platform expertise: Proven leadership in AWS and Azure enterprise environments; strong functional exposure to advanced analytics platforms such as Palantir Foundry/AIP Transformation leadership: Demonstrated success operating within a Transformation Office or enterprise innovation function—establishing operating models, reusable patterns, and governance to scale adoption responsibly Ability to translate architecture decisions into measurable outcomes, cost efficiency, operational resilience, and risk reduction Ability to meet or exceed Performance Competencies excellent judgment, troubleshooting, problem solving, analysis, and discretion; Computer keyboarding, travel as required Auditory/Visual : Hearing, vision and talking The statements contained in this document are intended to describe the general nature and level of work being performed by a colleague assigned to this description. They are not intended to constitute a comprehensive list of functions, duties, or local variances. Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience.