Sr. Director, AI Platform and Engineering

Job Description

As the Sr. Director, AI Platform and Engineering, you will play a key role in supporting The Mutual Group (TMG), Guide One Insurance, and future members by leading the technical design, engineering practices, and execution required to turn TMG’s AI-First IT vision into practical enterprise capability. This role is accountable for building reusable AI platforms, architecture patterns, engineering playbooks, and delivery practices that enable AI adoption across business processes, software delivery, and IT operations.

Information Technology

• Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in‑office schedule. The initial training period may require additional in‑office days.

• Define and lead the technical roadmap for TMG’s enterprise AI platform and AI engineering capabilities, aligned to business priorities, enterprise architecture, security standards, and governance expectations.
• Translate AI-First IT strategy into practical platform capabilities, reference architectures, engineering standards, reusable components, and delivery patterns.
• Evaluate AI platforms, cloud services, frameworks, vendor solutions, integration patterns, and development tools with a focus on security, reuse, interoperability, scalability, maintainability, and business value.
• Provide hands‑on technical leadership in architecture reviews, solution design, technical decision‑making, delivery planning, and complex problem‑solving.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, enterprise knowledge systems, and responsible deployment practices.

• Partner with business and technology teams to design and deliver AI‑enabled capabilities for underwriting, claims, operations, finance, customer service, and other enterprise functions.
• Translate business use cases into scalable technical solutions, including decision support, workflow automation, document intelligence, knowledge retrieval, summarization, classification, triage, and productivity tools.
• Establish repeatable technical patterns for moving AI use cases from proof of concept to secure, production‑ready adoption.
• Work with product, business, data, and risk partners to define solution feasibility, data needs, integration approach, human oversight, evaluation criteria, and operational readiness.
• Build reusable accelerators and implementation playbooks that allow similar AI capabilities to be deployed across multiple business processes with less rework.

• Define architecture patterns for AI‑enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Establish technical standards for model access, prompt and response handling, context management, retrieval‑augmented generation, semantic search, vector databases, observability, cost management, and production support.
• Lead delivery of foundational AI platform capabilities such as model gateways, model catalogs, orchestration layers, RAG frameworks, vector stores, embedding pipelines, evaluation frameworks, usage monitoring, and governance controls.
• Establish patterns for integrating AI capabilities with enterprise systems, APIs, data platforms, document repositories, workflow tools, service management platforms, and business applications.
• Create reusable engineering assets, templates, reference implementations, and deployment playbooks that improve delivery speed, quality, consistency, and reuse.

• Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.
• Define patterns for Agentic AI, including tool and function calling, workflow orchestration, human‑in‑the‑loop controls, memory and context management, guardrails, monitoring, and safe execution.
• Establish usage patterns for Model Context Protocol (MCP) or similar approaches…