Principal Architect - Application Development

Farmers Insurance

2d•$179,850 - $341,125•Hybrid

About The Position

The Principal Architect, Application Development is the senior technical authority for how Farmers designs, builds, and evolves business applications. The role owns application architecture standards, reference architectures, and engineering patterns across the portfolio, setting direction for cloud-native, API-first, and AI-enabled application delivery and ensuring software is secure, resilient, and maintainable at scale. The Principal Architect partners with engineering, product, security, data, and platform leaders to translate enterprise strategy into practical application designs. The role leads architecture decisions for complex, high-risk application initiatives, including legacy and core-system modernization; advances AI-native engineering and Spec-Driven Development (SDD); and mentors architects and senior engineers. It requires hands-on technical credibility and the ability to influence alignment across teams and business units. A defining accountability is architecting Farmers’ AI-native software development lifecycle (AI SDLC). The role owns the enterprise SDD standard, where versioned specifications are the primary engineering artifact. It establishes the stages from testable requirements through plans, tasks, implementation, verification, and promotion, along with guardrails, governing principles, human approval gates, and verification standards. The Principal Architect coaches teams to adopt these practices so AI improves delivery speed, quality, and consistency. The role also defines the agentic engineering toolchain and standards for verifying AI-generated software, leads architecture for modernization initiatives, governs design quality and conformance, and scales adoption of AI SDLC and SDD across delivery teams. The position may be performed in person at a Farmers office or virtually at an approved alternative work location. The work environment is indoors, climate-controlled, and open-office, with normal office noise and interruptions.

Requirements

High school diploma or equivalent required.
At least two architect or senior-level industry certifications required upon hire.
At least 12 years of architecture experience, including significant enterprise architecture leadership in complex, regulated enterprises.
Deep proficiency in at least one modern enterprise language and ecosystem, such as Java, C#/.NET, TypeScript/Node.js, or Python.
Strong command of API design and integration, including REST, GraphQL, messaging and streaming such as Kafka, domain-driven design, and secure-by-design principles.
Working knowledge of CI/CD, DevSecOps, infrastructure as code, observability, and site reliability practices.
Ability to communicate complex technical concepts clearly to engineers and executives and influence without direct authority.
Deep practical knowledge of Spec-Driven Development methods and tooling, including creating testable specifications, technical plans, and task breakdowns for AI agents.
Expertise with AI coding agents and assistants, including agent configuration, custom instructions, reusable skills, and multi-agent orchestration.
Strong grasp of prompt and context engineering, retrieval-augmented generation, Model Context Protocol, and large language model selection, evaluation, and cost management.
Ability to design verification approaches for non-deterministic AI output, including evaluation harnesses, golden datasets, acceptance oracles, and automated quality and security gates.
Understanding of AI governance and software-delivery risks, including responsible AI principles, data protection, prompt injection, software supply chain and licensing risk, and regulatory expectations for a regulated insurer.
Familiarity with engineering productivity measurement, such as DORA and SPACE metrics, and ability to build evidence-based business cases for AI SDLC investment.

Nice To Haves

Bachelor’s degree preferred.
Master’s degree preferred.
Architect-level industry certification, such as a cloud solutions architect professional certification.
AI or machine learning certification, such as Microsoft Azure AI Engineer Associate or AWS Certified Machine Learning, or equivalent demonstrated expertise.

Responsibilities

Define, own, and evolve the enterprise Spec-Driven Development standard and AI SDLC reference model, including specifications, plans, tasks, implementation, verification, promotion, governing principles, human-approved quality gates, and traceability.
Architect the agentic engineering toolchain, including AI coding agents and assistants, reusable skills and plugins, Model Context Protocol servers, model gateways and routing, and context engineering, with controls for security, data classification, cost, and auditability.
Own verification and evidence standards for AI-generated software, including acceptance oracles, requirement-to-test coverage, automated and agentic testing, fitness functions, and policy-as-code checks.
Define and own application architecture standards, reference architectures, and reusable patterns for microservices, event-driven architecture, API-first design, domain-driven design, and API and integration standards.
Publish architecture standards as machine-readable specifications and skills for engineers and AI agents.
Lead architecture for complex application and modernization initiatives, using AI-assisted reverse engineering to extract specifications from legacy and core systems and guide decomposition and migration to cloud-native platforms.
Govern application design quality through pull-request-based architecture review, architecture decision records, and automated conformance checks.
Resolve cross-team design conflicts and address AI-specific risks, including hallucinated dependencies, insecure generated code, and intellectual property exposure.
Scale AI SDLC and SDD adoption by coaching teams and architects, leading the engineering community of practice, and aligning SDD artifacts with enterprise delivery and work-management frameworks.
Measure AI SDLC outcomes, including cycle time, change failure rate, defect escape, and AI-assisted throughput.
Establish the vision and baseline, launch the enterprise AI SDLC, establish the AI engineering toolchain, and create architecture guardrails during the first year.
Communicate complex technical concepts to engineers and executives and influence without direct authority.