Principal Architect, AI Solutions

Principal Architect

We are seeking a Principal Architect to help lead the next generation of AI-enabled product, platform, and engineering capabilities across our organization. This is a senior individual contributor role operating at the highest levels of technical leadership. The ideal candidate will combine deep AI architecture expertise with strong enterprise architecture judgment, practical delivery experience, and the ability to influence teams across business units.

This role will partner closely with business and technology leaders to shape product and platform architectures, define and uphold enterprise standards, and help our engineering organization adopt modern AI and AI-powered software delivery practices responsibly and effectively.

Key responsibilities include:

• Leading architecture for strategic AI initiatives across products, platforms, and shared enterprise capabilities
• Partnering with business units, product leaders, and engineering teams to shape scalable, secure, and practical AI solution architectures
• Defining and evolving architecture standards, reference patterns, and guardrails for AI solutions and AI-enabled software development
• Evaluating emerging AI technologies, platforms, and patterns, and guiding the organization on where and how to adopt them
• Providing architectural leadership for capabilities, agentic workflows, AI orchestration, evaluations, AI observability, and AI governance
• Ensuring AI solutions align with enterprise requirements for security, privacy, compliance, resiliency, observability, and maintainability
• Helping engineering teams leverage AI-powered PDLC capabilities across coding, testing, documentation, analysis, and delivery workflows
• Guiding architecture decisions across application, data, integration, platform, and operational concerns to avoid fragmented or duplicative solutions
• Mentoring technical leads and senior engineers and contributing to raising the technical bar across the organization
• Participating in architecture reviews, technical strategy discussions, and investment decisions related to AI and platform modernization

Required qualifications include:

• Bachelor's or master's degree in Computer Science, Engineering, or related field, or equivalent practical experience
• 12+ years of experience in software engineering, architecture, or technology leadership roles
• Significant experience designing and guiding enterprise-grade AI solutions in production environments
• Deep knowledge of modern AI architecture patterns, including LLM-based applications, retrieval systems, agentic workflows, prompt and tool orchestration, and evaluation approaches
• Strong architecture background across distributed systems, APIs, integration, data platforms, security, and cloud-native solutions
• Experience establishing architecture standards, reference architectures, and technical guardrails across multiple teams
• Proven ability to influence senior technical and business stakeholders without direct authority
• Strong written and verbal communication skills, with the ability to tailor communication from engineering teams to executive leadership
• Demonstrated ability to balance innovation, speed, risk management, and long-term maintainability

Preferred qualifications include:

• Experience working in a large enterprise, platform-oriented environment
• Experience with AI governance, responsible AI, model risk, privacy, and security controls
• Familiarity with cloud AI platforms and services, vector search, AI gateways, observability, and production AI operations
• Experience helping engineering organizations adopt AI-powered development tools and practices
• Experience defining reusable architecture patterns for shared enterprise AI capabilities
• Experience mentoring architects or leading architecture communities of practice
• Master's degree in Computer Science, Engineering, Data Science, or related field

What success looks like:

• Business units have a strong architecture partner for AI-driven product and platform decisions
• The organization has clearer standards and reusable patterns for AI solutions
• Teams adopt AI technologies faster and more safely
• AI initiatives are better aligned to business value, enterprise…