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,…