Principal AI Engineering Architect

The Mutual Group

Principal AI architect designing secure, reusable Generative AI platforms and enterprise integrations. Supporting insurance functions including underwriting, claims, operations, finance, and customer service.

Posted 8/5/2026full-timeDes Moines • Illinois, Iowa, Massachusetts, Texas, Washington • 🇺🇸 United StatesLead💰 $170,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining architecture patterns and technical standards for AI-enabled applications, with a strong focus on Generative AI and Agentic AI solutions. Capable of translating complex business and technology use cases into scalable solution architectures while ensuring security, compliance, and operational readiness.

Highest-signal resume keywords

Generative AI PatternsAgentic AI PatternsArchitecture GovernanceAPIs and MicroservicesCloud-Native Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Architecture PatternsTechnical StandardsAI-Enabled ApplicationsData IntegrationAutomation CapabilitiesModel Context ProtocolPrompt EngineeringEvent-Driven DesignIntegration FrameworksProduction-Grade Platforms

Soft Skills

CollaborationLeadershipCommunicationProblem-SolvingDocumentation

Tools & Technologies

Vector DatabasesSemantic SearchObservability ToolsDeveloper Productivity ToolsCI/CD

Industry Keywords

Regulated EnvironmentsSecurityPrivacyRisk ManagementCompliance

Tech Stack

Tools & technologies

CloudCyber SecurityMicroservices

About the role

Key responsibilities & impact

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 covering application design, data flows, integrations, model usage, security, and operations
Shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks
Lead design reviews, technical decisions, proof-of-concept evaluations, implementation planning, and production readiness
Design reusable AI platform patterns for model access, RAG, vector databases, semantic search, embeddings, enterprise knowledge integration, prompt and response handling, and observability
Define integrations connecting AI capabilities with enterprise systems, APIs, data platforms, repositories, workflow tools, service management platforms, and business applications
Create architecture blueprints, technical standards, reusable components, templates, and implementation guidance
Guide build-versus-buy, platform selection, vendor, interoperability, scalability, maintainability, and cost-effectiveness decisions
Guide implementation of Generative AI and Agentic AI solutions, including model usage, tool calling, orchestration, human oversight, context, memory, guardrails, and safe execution
Establish MCP or similar usage patterns for secure connections to enterprise tools, data sources, APIs, and workflow actions
Support model experimentation, evaluation, validation, monitoring, drift detection, feedback loops, and responsible deployment
Partner with business, product, data, technology, security, infrastructure, operations, and governance teams on AI-enabled solutions
Design solutions for underwriting, claims, operations, finance, customer service, and other enterprise functions
Evaluate feasibility, data readiness, integration complexity, user experience, human oversight, and operational support requirements
Embed secure-by-design, privacy-by-design, and responsible AI practices into solution architecture
Define controls for identity and access management, sensitive data, logging, output validation, human oversight, vendors, and production readiness
Participate in architecture governance, design reviews, technical risk assessments, and production readiness reviews
Promote documentation, testability, traceability, performance, and clear support models

Requirements

What you’ll need

10+ years of progressive technology experience across software engineering, architecture, platform engineering, cloud, data, integration, automation, AI, or enterprise technology delivery
5+ years of experience with AI, machine learning, automation, advanced analytics, intelligent platforms, developer productivity tools, or emerging technology capabilities
Strong technical depth in Generative AI patterns including LLMs, SLMs, embeddings, prompt engineering, RAG, vector databases, semantic search, evaluation frameworks, and enterprise knowledge integration
Experience with Agentic AI patterns including agents, tool/function calling, orchestration, human-in-the-loop workflows, context management, guardrails, monitoring, and safe deployment
Familiarity with Model Context Protocol (MCP) or similar approaches
Strong understanding of APIs, microservices, event-driven design, cloud-native platforms, data integration, DevSecOps, CI/CD, observability, cybersecurity, identity, and privacy
Proven experience designing production-grade enterprise platforms, reusable architecture patterns, integration frameworks, automation capabilities, or developer productivity solutions
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related field required
Experience in regulated environments with security, privacy, risk, compliance, auditability, and operational readiness preferred
Successful completion of a background check
Authorization to work in the U.S. required through employment verification

Benefits

Comp & perks

Competitive base salary plus incentive plans for eligible team members
401(K) retirement plan with a company match of up to 6% of eligible salary
Free basic life and AD&D insurance
Long-term disability and short-term disability insurance
Medical, dental and vision plans
Wellness incentives
Generous time off program including personal, holiday and volunteer paid time off
Flexible work schedules and hybrid/remote options for eligible positions
Educational assistance