Principal Architect

The Principal AI Architect (P6) is a senior individual contributor responsible for defining and implementing enterprise AI architecture across the organization. This role operates at a director-equivalent level of influence while remaining hands-on in design, prototyping, and implementation.

The architect will lead the design of the enterprise AI platform on AWS, establish architectural standards, and deliver production AI solutions end-to-end. This role requires a practitioner who has already implemented enterprise AI systems in real environments and can operate at both strategic and technical depth.

This position partners closely with business leaders, data governance, DevOps, and engineering teams to translate use cases into scalable, secure, enterprise-grade AI capabilities.



Day to Day:

• Design and implement enterprise AI architecture and platform strategy on AWS

• Evaluate and select AI solution patterns (RAG frameworks, vector database strategies, model hosting approaches)

• Architect and deploy AI solutions end-to-end from proof of concept to production

• Provide solution architecture support across enterprise AI use cases

• Build rapid prototypes and technical proofs to validate architecture decisions

• Define standards for enterprise AI design, deployment, and governance

• Partner with business stakeholders and executives to align AI initiatives to outcomes and ROI

• Collaborate with data governance, DevOps, and platform teams to operationalize AI capabilities

• Mentor architects and engineers on enterprise AI best practices

• Drive platform-level architectural decisions rather than isolated use case solutions

• 15 years of engineering and architecture experience

• Career progression from software engineer to architect

• Proven experience architecting enterprise-scale platforms

• At least 1 year hands-on implementing enterprise AI architecture in production

• Demonstrated experience deploying AI solutions end-to-end (not just pilots or research)

• Strong AWS architecture experience

• Ability to be hands-on with prototyping, coding, and solution validation

• Experience working directly with senior business stakeholders or executives

• Deep architectural maturity and decision-making experience

• Experience designing RAG architectures and vector database strategies

• Experience building enterprise AI or ML platforms

• MLOps architecture and model lifecycle management experience

• Background in machine learning engineering or applied AI systems

• Experience in regulated or financial services environments

• Experience defining enterprise architecture standards or strategy

• Familiarity with AI governance, security, and responsible AI frameworks