Principal AI Engineering Architect

Department:

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 inoffice 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 production deployment.

• Help engineering teams design AI solutions that are accurate observable explainable where appropriate cost-aware and aligned with business and risk expectations.

Business Solution Architecture & Enterprise Adoption

• 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 experience human oversight and operational support requirements.

• Create reusable patterns that allow similar AI capabilities to be deployed across multiple business processes with less rework.

• Communicate architecture decisions clearly to technical and non-technical stakeholders including tradeoffs risks dependencies and implementation options.

Security Governance & Engineering Quality

• Partner with Security Data Architecture and AI & Technology Risk Governance teams to embed secure-by-design privacy-by-design and responsible AI practices into solution architecture.

• Define technical controls for identity and access management sensitive data handling prompt and response logging output validation human oversight vendor integration and production readiness.

• Ensure AI solutions align with enterprise standards for security privacy auditability observability resilience compliance and operational support.

• Participate in architecture governance design reviews technical risk assessments and production readiness reviews.

• Promote engineering quality through strong documentation testability traceability performance considerations and clear support models.

Qualifications:

• 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 for securely connecting AI systems to enterprise tools data sources APIs and workflow actions.

• Strong understanding of modern architecture patterns including 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.

• Experience working in regulated environments with security privacy risk compliance auditability and operational readiness expectations preferred.

• Bachelors degree in Computer Science Engineering Information Systems Data Science or related field required. Masters degree preferred.

Leadership Attributes

• Deeply technical architect who can dive into details while keeping architecture practical scalable and business aligned.

• Strong systems thinker who simplifies complexity into reusable patterns standards and clear technical guidance.

• Hands-on problem solver with strong analytical reasoning and sound judgment.

• Collaborative partner who builds trust across business engineering data security infrastructure operations and governance teams.

• Constructive challenger who raises technical standards while helping teams move quickly and responsibly.

• Strong communicator who can explain complex AI and architecture concepts in clear actionable terms.

Pay Range:

Anticipated Hiring Range:

• $170000 - $200000annual base salary depending on experience qualifications and geographic location

Benefits:

We are proud to offer our full-time regular employees a robust benefits suite that includes:

• Competitive base salary plus incentive plans for eligible team members

• 401(K) retirement plan that includes a company match of up to 6% of your eligible salary

• Free basic life and AD&D long-term disability and short-term disability insurance

• Medical dental and vision plans to meet your unique healthcare needs

• Wellness incentives

• Generous time off program that includes personal holiday and volunteer paid time off

• Flexible work schedules and hybrid/remote options for eligible positions

• Educational assistance

Equal Opportunity Employer

The Mutual Groupis an Equal Opportunity Employer. It is our policy to recruit hire train and promote individuals in all job classifications without regard to race color religion sex national origin age veteran status disability sexual orientation gender identity or any other characteristic protected by law.

• Know Your Rights: Workplace Discrimination is Illegal

• Your Rights Under USERRA

Applicants requiring a reasonable accommodation due to a disability at any stage of the employment application process should contact.

Employment Verification

The Mutual Group participates in theE-Verifyprogram and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. You are protected fromemployment discriminationbased on your citizenship status and national origin.

E-Verify Program Overview

E-Verify Participation Poster

All offers of employment are contingent upon the successful completion of a background check.

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