Principal AI Engineering Architect

Job Description

As the Principal AI Engineering Architect, you will play a key role in supporting The Mutual Group (TMG), Guide One Insurance, and future members by defining and guiding the technical architecture for AI-first engineering, secure AI platforms, reusable components, integration patterns, and scalable technical standards across TMG. This is a senior individual contributor role for a deeply technical architect who can translate complex business and technology needs into practical, secure, and reusable AI-enabled solutions.

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 architecture patterns and technical standards for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Translate business and technology use cases into scalable solution architectures, including application design, data flows, integration patterns, model usage, security controls, and operational requirements.
• Partner with the Sr. Director, AI Platform and Engineering to shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks.
• Provide hands‑on architecture leadership in design reviews, technical decision‑making, proof‑of‑concept evaluation, implementation planning, and production readiness.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, cloud AI services, enterprise knowledge systems, and secure deployment practices.

• Design reusable platform patterns for model access, retrieval‑augmented generation, vector databases, semantic search, embeddings, enterprise knowledge integration, prompt and response handling, and AI observability.
• Define integration patterns for connecting AI capabilities with enterprise systems, APIs, data platforms, document repositories, workflow tools, service management platforms, and business applications.
• Create architecture blueprints, technical standards, reusable components, templates, and implementation guidance that improve speed, consistency, quality, and reuse.
• Guide decisions on build versus buy, platform selection, vendor capabilities, interoperability, scalability, maintainability, and cost effectiveness.
• Ensure AI platform patterns are designed for secure production use, including reliability, monitoring, access control, auditability, and lifecycle management.

• Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.
• Define technical patterns for Agentic AI, including tool and function calling, workflow orchestration, human‑in‑the‑loop controls, context management, memory patterns, guardrails, monitoring, and safe execution.
• Establish usage patterns for Model Context Protocol (MCP) or similar approaches for securely connecting AI systems to enterprise tools, data sources, APIs, and workflow actions.
• Support practices for model selection, experimentation, evaluation, validation, performance monitoring, drift detection, feedback loops, and responsible production deployment.
• Help engineering teams design AI solutions that are accurate, observable, explainable where appropriate, cost‑aware, and aligned with business and risk expectations.

• Partner with business, product, data, and technology teams to design AI‑enabled solutions for underwriting, claims, operations, finance, customer service, and other enterprise functions.
• Translate business needs into practical AI architectures for decision support, workflow automation, document intelligence, knowledge assistance, triage, summarization, and productivity improvement.
• Help teams evaluate feasibility, data readiness, integration complexity, user…