Principal Solution Architect: AI & Platform Modernization

Cox Automotive Inc.

Atlanta (GA)

On-site

USD 163,000 - 272,000

Full time

4 days ago

Be an early applicant

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

Cox Automotive's architecture group is reimagining the car-buying and service experience across the retail lifecycle. The Principal Solution Architect provides technical leadership to advance platform modernization, solution architecture, and AI initiatives across CAI services.

Key responsibilities include driving solution design, prioritizing architectural investments, championing reusable patterns, and leading AI capabilities and PoCs to reduce delivery risk and enable scalable API delivery in

Qualifications

Bachelor's degree in a related discipline with 10 years' experience or an equivalent combination.
Ability to align diverse stakeholders around technical direction across teams.
Highly analytical with capacity to dive technically deep and prototype rapidly.
Breadth of architecture knowledge spanning application, integration and platform domains.
Extensive experience in API delivery at scale and in cloud environments (AWS preferred).
Hands-on experience with AI technologies and concepts (LLMs, vector databases, agent-based architecture).
Experience in DevOps cultures, automation, and continuous delivery.

Responsibilities

Drive solution design from discovery through delivery, shaping epics and surfacing risks.
Partner with engineering, product, and agile leaders to sequence architectural investments.
Champion adoption of Common tenancy and AWS-native patterns across the value stream.
Identify and champion generative AI capabilities for new products and features.
Produce architectural artifacts—roadmaps, diagrams, integration models—for teams.
Assess emerging AI tech and ensure governance, security, and enterprise standards.
Promote reusable patterns to accelerate product development and AI adoption.
Translate business objectives into practical solution options and trade-offs.
Build AI capabilities in engineering teams through mentoring and reviews.
Lead proofs of concept and reference implementations to validate assumptions.