Principal AI Engineering Architect

Department:

Information Technology

Job Description:

As the Principal AI Engineering Architect, you will play a key role in supporting The Mutual Group (TMG), GuideOne 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.

This role will work across AI-First IT, Applications, Engineering, Data, Infrastructure, Operations, Security, Architecture, and business teams to design AI capabilities that can move from concept to production with the right architecture, controls, integration model, and operational readiness. The Principal AI Engineering Architect will be expected to stay close to the work, review designs, guide engineering teams, solve difficult technical problems, and create patterns that can be reused across multiple initiatives.

The role will have deeper focus on AI-enabled business processes and AI-first future platforms, while also supporting AI adoption across the IT SDLC and IT operations. The successful candidate will bring strong technical judgment, hands-on architecture depth, and the ability to simplify complex AI engineering concepts into standards, blueprints, and implementation guidance that broader teams can adopt.

Work Arrangement:

• 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.

Accountabilities:

Architecture Strategy & Technical Direction

• 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.

AI Platform Architecture & Reusable Engineering Patterns

• 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.

GenAI, Agentic AI & Model Engineering

• 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 produc…