Sr. Director, AI Architect

About Patra Patra is an outcome-focused operating partner working exclusively for the insurance industry. Combining expert global teams, process intelligence, and purpose-built technology, Patra delivers measurable results to clients. With 20 years of deep domain expertise, exceptional client retention rates, and significant sustained investment in AI, Patra partners with agencies, brokers, MGAs, MGUs, and carriers to transform how insurance operations are delivered. Core Duties AI and data are becoming foundational capabilities across Patra’s technology organization. The AI & Data Architecture function establishes the enterprise architecture, platforms, governance, and shared capabilities that enable product teams to build intelligent applications safely and at scale. As AI Architect, you will lead Patra’s AI & Data Architecture function, operating at a Sr. Director-level scope with responsibility for its strategy, team formation and management, systems architecture, strategic priorities, and, most importantly, reliable and impactful execution. The function has particular responsibility for document-intensive and unstructured-data environments, including retrieval-augmented generation, semantic and vector search, AI agents, evaluation datasets, enterprise analytics, and granular AI observability. You will report to the Chief Digital Officer (CDO) and partner with senior leaders across Engineering, Product, Security, Compliance, Business Intelligence, Operations, and other business functions. What you'll do • Lead the AI & Data Architecture function. Own the strategy, organizational structure, technical direction, priorities, delivery, quality, and operating model for Patra’s enterprise AI and data capabilities. • Form and lead the organization. Determine staffing requirements and build multidisciplinary teams spanning AI engineering, data engineering, machine learning, enterprise data architecture, analytics infrastructure, and shared AI services, while personally delivering on urgent business priorities at the same time • Manage talent and performance. Establish objectives for direct reports as your team grows, evaluate performance, provide career and leadership development, and exercise delegated authority to make or recommend decisions concerning hiring, promotions, compensation, organizational assignments, and other personnel actions. • Own the enterprise AI and data architecture. Establish Patra’s target-state architecture for how data is ingested, governed, modeled, stored, streamed, retrieved, analyzed, and consumed by enterprise applications, analytics platforms, and AI systems. • Direct architecture for document-intensive and unstructured domains. Establish enterprise standards and approve architectural approaches for document ingestion, metadata extraction, indexing, semantic retrieval, vector search, knowledge enrichment, and AI consumption. Detailed design and implementation are delegated to subordinate professional teams. • Industrialize the Agentic AI platform. Direct the evolution of agentic capabilities into production-grade enterprise infrastructure supporting agent orchestration, tool integration, retrieval-augmented generation, model interaction, security boundaries, evaluation, observability, and appropriate human oversight. • Own AI evaluation and data strategy. Establish the standards and organizational processes governing training, fine-tuning, benchmarking, and evaluation datasets, including quality, provenance, versioning, access, retention, and appropriate use. • Treat AI observability as a strategic data asset. Establish the architecture and governance for capturing and using event streams across agent activity, model interactions, retrieval operations, tool execution, quality signals, latency, cost, failures, and human interventions. • Lead the enterprise data platform strategy. Set direction for cloud-native data capabilities including AWS services such as S3, Redshift, streaming and event technologies, together with platforms such as Snowflake and/or Databricks where appropriate. • Establish enterprise data standards. Define and govern modern approaches to operational, analytical, event-driven, semantic, and AI-oriented data modeling and ensure common architectural standards are adopted across engineering teams. • Make Business Intelligence a first-class platform stakeholder. Ensure enterprise data capabilities support analytics, operational reporting, experimentation, decision support, and executive intelligence without relying on fragmented downstream data extraction. • Build governance into the architecture. Establish standards for data ownership, lineage, provenance, classification, quality, lifecycle management, access control, AI usage, auditability, and enterprise data governance. • Protect customer and tenant data. Establish architecture and controls for strict multi-tenant client-data segregation across storage, processing, analytics, retrieval, model interaction, evaluation, and observability systems. • Own jurisdiction-aware data architecture. Establish retention, archival, residency, and deletion strategies supporting differing contractual, regulatory, and jurisdictional requirements. • Set quality and reliability expectations. Establish SLAs, SLOs, quality metrics, and operational standards for critical data assets, pipelines, AI services, retrieval systems, and shared platform capabilities. • Establish reusable platform capabilities. Direct the development of common capabilities for identity, access, observability, model access, AI evaluation, security, compliance controls, tenancy, CI/CD, and data services that can be consumed across product organizations. • Direct enterprise integration architecture. Establish standards for integrating AI and data platforms with Patra products, internal business systems, partner platforms, APIs, event streams, and external data providers. • Run AI and data capabilities as enterprise products. Treat product engineers, analytics teams, business stakeholders, and other internal organizations as customers of the platform. Establish adoption, reliability, quality, and developer-productivity measures for shared capabilities. • Own architecture governance. Lead reviews of material AI, data, and platform architecture decisions; establish reference architectures and engineering standards; approve significant architectural approaches; and resolve cross-organizational technical decisions. • Direct the AI and data portfolio. Establish priorities across concurrent AI, data, and platform programs and allocate engineering and data-science resources according to business value, organizational capacity, technical dependencies, and enterprise risk. • Manage technology investment. Participate in annual planning, determine staffing and platform requirements, develop budget recommendations, evaluate strategic technology vendors, and make or recommend investment decisions involving AI, cloud, data, and infrastructure platforms. • Shape the multi-year technology roadmap. Partner with executive engineering and product leadership to define Patra’s AI and data roadmap and translate strategic objectives into sequenced, staffed, and measurable organizational programs. • Represent the function with senior leadership. Advise executives on AI strategy, enterprise data architecture, platform investments, organizational capacity, delivery risk, AI risk, governance, and major technology trade-offs. How You Think About AI And Data Platforms We are looking for a leader whose operating instincts align with how we build. • Build shared capabilities once. Identity, observability, model access, evaluation, security, governance, tenancy, CI/CD, and data access should be established as reusable platform capabilities rather than repeatedly implemented by individual teams. • Data is a product. Enterprise data, documents, application events, model interactions, evaluation results, and observability signals are strategic assets with defined owners, contracts, quality expectations, governance, and consumers. • AI-ready from ingestion onward. Documents, metadata, events, permissions, lineage, and semantic context should be designed so that they can safely support applications, analytics, machine learning, retrieval, and agentic systems. • Governance is architecture. Customer isolation, security, retention, residency, auditability, Responsible AI, and regulatory considerations are architectural requirements rather than controls added after systems have been built. • Evaluation precedes confidence. Production AI requires measurable success criteria, curated evaluation datasets, regression testing, observability, and mechanisms for detecting degraded behavior. • Observability is a first-class data asset. Agent activity, retrievals, model calls, tool calls, cost, latency, failures, quality signals, user feedback, and human interventions should produce structured and governed telemetry. • Automation over operational heroics. Repeated manual intervention represents an architectural or platform gap. Provisioning, deployment, evaluation, governance checks, and operational processes should increasingly be automated. • Self-service over central bottlenecks. The AI & Data Architecture organization exists to increase the leverage of engineering and product teams, not to become a ticket queue they must pass through. • Human oversight is intentional. Increasing autonomy should be accompanied by explicit decision boundaries, escalation mechanisms, auditability, and controls appropriate to the risk of the workflow. • Incremental over big-bang. Major platform transitions should produce measurable production value incrementally, with explicit migration plans and accountable milestones. • Specifications precede implementation. As AI accelerates implementation, clear requirements, contracts, architectural boundaries, evaluation criteria, and ownership become more important. • Decisions are explicit. Make trade-offs visible across cost, risk, reliability, security, scalability, and time-to-value. Once a decision is made, establish ownership and execute against it. Qualifications • Technical and organizational depth. 10+ years of progressive experience across software engineering, enterprise architecture, data engineering, cloud platforms, artificial intelligence, or machine learning, including significant experience leading senior technical professionals. • Organizational leadership. Demonstrated experience forming, scaling, and leading multidisciplinary engineering and data organizations. • People management. Experience hiring professional employees, developing managers and technical leaders, evaluating performance, making promotion and compensation recommendations, planning organizational capacity, and allocating resources across teams and strategic initiatives. • Enterprise AI credibility. Deep understanding of production AI systems including LLM applications, RAG, embeddings, semantic and vector search, agentic architectures, AI orchestration, evaluation, observability, governance, and human-in-the-loop systems. • Enterprise data architecture. Significant experience directing the architecture of platforms supporting structured, semi-structured, and unstructured enterprise data. • Document-intensive domains. Experience with architectures where documents and unstructured information are important business assets supporting operational workflows, search, analytics, AI, and governance. • Cloud and modern data platforms. Experience establishing strategy around AWS-native data services including S3, Redshift, streaming and event technologies, as well as platforms such as Snowflake and/or Databricks. • Modern data architecture. Strong understanding of operational, analytical, dimensional, event-oriented, lakehouse, semantic, and domain-oriented data architectures and their trade-offs. • Production responsibility. Experience leading organizations responsible for enterprise reliability, availability, security, performance, and operational-readiness requirements. • Governance depth. Experience establishing enterprise standards around data quality, ownership, lineage, retention, tenant segregation, access control, auditability, and lifecycle management. • Regulated environments. Experience operating in environments where contractual, security, privacy, regulatory, data-residency, or jurisdiction-specific requirements materially influence architecture. • Strategic delivery leadership. Demonstrated ability to translate broad technology strategy into prioritized, funded, staffed, and measurable programs executed through multiple professional teams. • Executive communication. Ability to advise senior executives on architecture, investment priorities, organizational capacity, technology strategy, and risk while maintaining credibility with senior engineers and architects. • Talent development. A track record of building organizations whose managers and technical leaders can independently own significant areas of responsibility. Nice to have • Experience with enterprise technology supporting insurance, financial services, healthcare, utilities, government, or similarly regulated industries. • Experience building shared AI platforms consumed by multiple products or engineering organizations. • Experience deploying agentic systems at production scale with enterprise evaluation, governance, observability, cost controls, and human oversight. • Experience leading large-scale modernization from legacy systems to cloud-native data and AI platforms. • Experience operating internal platforms using measurable adoption, reliability, developer-experience, and business-value metrics. • Experience designing global platforms with customer isolation, data residency, sovereignty, retention, and jurisdiction-dependent requirements. • Experience establishing Responsible AI, AI-governance, model-risk, traceability, or human-oversight frameworks. Equal Employment Opportunity Patra Corporation is an equal opportunity employer committed to celebrating diversity and creating a safe and inclusive environment for all employees.