VP, AI – IT and Transformation

Responsibilities

• Define and lead TMG’s enterprise AI strategy in partnership with the CITO, business executives, technology leaders, and risk governance partners.
• Establish the AI-First IT operating model, roadmap, governance approach, funding priorities, delivery rhythm, and measurable outcomes.
• Build a disciplined portfolio of AI initiatives that balances experimentation, speed, security, business value, operational readiness, and risk management.
• Translate emerging AI capabilities into practical, secure, scalable solutions that improve business outcomes, employee productivity, and technology delivery.
• Serve as a thought leader and practical operator who helps the organization understand where AI can create value, where it creates risk, and how to adopt it responsibly.
• Lead the strategy and delivery of foundational AI platform capabilities that support secure, scalable, and reusable AI-enabled applications.
• Define architecture patterns for AI-First applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Guide platform capabilities such as model access, retrieval frameworks, vector databases, enterprise knowledge integration, prompt and response controls, observability, and governance guardrails.
• Introduce AI-assisted software engineering practices across the SDLC, including coding, testing, documentation, requirements analysis, code review, and engineering workflow automation.
• Partner with Applications, Engineering, Infrastructure, Operations, Architecture, Security, and Data teams to pilot, refine, and scale AI-enabled practices over time.
• Partner with underwriting, claims, operations, finance, customer service, and other business functions to identify and deliver high-value AI-enabled process improvements.
• Lead the development of AI capabilities such as decision support, workflow automation, document intelligence, knowledge assistance, summarization, triage, productivity tools, and service quality improvements.
• Help business teams move from AI ideas to practical use cases with clear outcomes, adoption plans, controls, and value measures.
• Lead enterprise enablement of AI productivity tools such as ChatGPT, Microsoft Copilot, and related assistants, including standards, training, adoption practices, and usage guardrails.
• Build reusable playbooks, enablement models, and communities of practice that raise AI fluency across IT and the broader organization.
• Work closely with the Senior Director, AI & Technology Risk Governance to ensure AI adoption is responsible, secure, compliant, and aligned with TMG’s risk appetite.

• 15+ years of progressive technology leadership experience, including senior responsibility for engineering, architecture, platforms, data, infrastructure, automation, AI, digital transformation, or enterprise technology delivery.
• Significant hands‑on leadership experience with AI, machine learning, Generative AI, automation, advanced analytics, intelligent platforms, developer productivity tools, or emerging technology capabilities.
• Strong understanding of Generative AI concepts and implementation patterns, including LLMs, SLMs, embeddings, prompt engineering, retrieval‑augmented generation, vector databases, semantic search, evaluation frameworks, and enterprise knowledge integration.
• Experience with Agentic AI patterns, including autonomous or semi‑autonomous agents, tool/function calling, workflow orchestration, human‑in‑the‑loop controls, guardrails, monitoring, and safe deployment practices.
• Familiarity with Model Context Protocol (MCP) or similar approaches for connecting AI systems to enterprise tools, data sources, APIs, and workflow actions in a secure and governed manner.
• Understanding of AI/ML model lifecycle practices, including model selection, experimentation, validation controls, performance monitoring, drift detection, feedback loops, auditability, and responsible production deployment.
• Familiarity with enterprise AI platform capabilities such as model access gateways, model catalogs, AI orchestration layers, policy enforcement, prompt and response controls, observability, cost monitoring, and usage…