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About This Role
United States \- Remote; Canada \- Remote
Business Technology/Full time/Remote
AI Architect is a technical leader reporting to the Director, Enterprise App Engineering. This role defines and drives Guidewire's enterprise AI strategy — translating business objectives across GTM, Finance, HR, Legal, and Engineering into production\-grade AI systems that deliver measurable impact. The architect owns end\-to\-end design across ingestion, orchestration, agent frameworks, security, compliance, and delivery.
The ideal candidate has led large, complex enterprise AI programs in production environments — not just proof\-of\-concepts. They bring equal fluency in LLM orchestration, full\-stack AWS architecture, enterprise security and compliance, and the stakeholder management required to embed AI into how a global company operates.
Job Description
Responsibilities:
Define and own Guidewire's enterprise AI architecture vision — establishing technical direction, design standards, and reference implementations across all BizTech AI initiatives.
Architect and lead delivery of production AI systems: multi\-agent orchestration platforms, RAG and GraphRAG knowledge systems, LLM\-powered workflows, and enterprise context graph initiatives spanning all Systems of Record.
Own the full\-stack AI architecture — from AWS infrastructure and data pipelines (Kafka, Iceberg, dbt, pgvector) through LLM gateway design, agent orchestration (LangGraph or equivalent), and user interfaces.
Help define enterprise AI security and governance standards — covering IAM, ABAC policy enforcement, PII handling, prompt injection defence, hallucination detection, and audit trail design for SOX and GDPR compliance.
Lead complex, multi\-team AI programs end to end — from stakeholder alignment and architectural design through delivery, adoption, and measured business outcomes across concurrent initiatives.
Help drive technology evaluation and build\-vs\-buy decisions across the AI tooling portfolio
Partner with Data Governance, Security, Legal, and Compliance teams to embed AI responsibly across the organisation.
Mentor senior engineers and foster a culture of technical excellence, pragmatic innovation, and accountable delivery across the BizTech organization.
Required Skills and Experience:
Bachelor's Degree in Computer Science, Engineering, or equivalent work experience.
12\+ years in enterprise software architecture, with at least 2 years designing and delivering production AI/ML or LLM\-based systems at scale.
Proven track record leading large, complex enterprise AI programs — multi\-year, multi\-team initiatives with clear, measurable business outcomes.
AWS expertise: Bedrock, EKS, Lambda, RDS, S3, Kinesis, IAM, VPC. AWS Solutions.
Full\-stack AI fluency: event ingestion and streaming pipelines, vector and graph databases, LLM orchestration and agent frameworks, API design, and front\-end delivery.
Experience with AI observability and evaluation — trace instrumentation, eval frameworks, and production monitoring (Datadog LLM Observability, LangSmith, or equivalent).
iPaaS and integration architecture experience — Workato, MuleSoft, or equivalent for enterprise SoR integration and event\-driven automation.
5\+ years of technical leadership — mentoring senior engineers, driving architecture strategy, and managing cross\-functional stakeholder relationships.
Excellent communication and executive presence — able to present complex AI architecture decisions clearly to both engineering teams and senior business leadership.
Demonstrated experience shipping Generative AI systems in production enterprise environments, with measurable impact on business outcomes.
The US base salary range for this full\-time position is $132,000 \- $198,000\. Your base pay will depend on your experience, skills, education, training, and location among other factors. All full\-time positions or part\-time roles working 30 hours or more a week at Guidewire are eligible for benefits that support their health and well\-being including health, dental, and vision insurance, paid time off, and a company sponsored retirement plan. In addition, some roles may be eligible for the annual company bonus plan, commissions, and/or long term incentive awards which are contingent on a variety of factors including, but not limited to, company and employee performance.
Disability Accommodations and Guidewire’s Appeals Process. Guidewire provides accommodations to the hiring process to create a fair opportunity for candidates with disabilities to contend for open positions. Accommodation requests should be directed to [email protected]. If things do not go as hoped, we invite you to use our appeals process. Guidewire promises to independently review any denied accommodation and any decision not to offer you the position. The appeals process is the same in either case. Within five business days of receiving a notice of denial of an accommodation, or receiving a notice of your non\-selection for a vacancy, e\-mail [email protected] to make an appeal. Guidewire will assign a new decision\-maker to review the request and/or hiring decision, who will then notify you in writing of a decision within 10 business days.
Interested in this position?
About Guidewire
Guidewire is the platform P\&C insurers trust to engage, innovate, and grow efficiently. We combine digital, core, analytics, and AI to deliver our platform as a cloud service. More than 540\+ insurers in 40 countries, from new ventures to the largest and most complex in the world, run on Guidewire.
As a partner to our customers, we continually evolve to enable their success. We are proud of our unparalleled implementation track record with 1600\+ successful projects, supported by the largest R\&D team and partner ecosystem in the industry. Our Marketplace provides hundreds of applications that accelerate integration, localization, and innovation.
For more information, please visit www.guidewire.com and follow us on Twitter: @Guidewire\_PandC.
Guidewire Software, Inc. is proud to be an equal opportunity and affirmative action employer. We are committed to an inclusive workplace, and believe that a diversity of perspectives, abilities, and cultures is a key to our success. Qualified applicants will receive consideration without regard to race, color, ancestry, religion, sex, national origin, citizenship, marital status, age, sexual orientation, gender identity, gender expression, veteran status, or disability. All offers are contingent upon passing a criminal history and other background checks where it's applicable to the position.
Salary Context
This $132K-$198K range is below the median for AI Architect roles in our dataset (median: $169K across 31 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,823 AI roles we're tracking, AI Architect positions make up 1% of the market. At Guidewire, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $212,500 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $132K to $198K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Guidewire AI Hiring
Guidewire has 2 open AI roles right now. They're hiring across AI Architect, MLOps Engineer. Positions span Remote, US, San Mateo, CA, US. Compensation range: $198K - $247K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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