AI Architect

Required Education (Precise)
" Bachelor's degree in Computer Science, Software Engineering, or related field (minimum)
o Master's + 3 years
o Bachelor's + 5+ years
o Associate's + 9 years
" Internships not accepted as experience

Preferred Education
" Master's degree in Computer Science or Software Engineering (preferred)

Preferred Certification
" TOGAF certification (nice-to-have)

Required Skills
" Cursor, Claude Code, or GitHub Copilot (or similar AI coding tools)
" Java/Spring Boot
" Python
" Distributed systems
" APIs and integration services
" Cloud-native platforms
" Software architecture and engineering best practices
" Prompt engineering
" Agentic development workflows
" Engineering metrics and productivity measurement
" Infrastructure and cloud
" Docker, containers, Kubernetes
" IT security, server/storage
" Security standards
" DevOps

Technical Desired
" Specification-driven development
" Robotics, Physical AI, Simulation, or Digital Twin
" AWS or Azure
" Developer experience platforms

Soft Skills Required
" Problem-solving and analytical thinking
" Agile/Scrum team collaboration
" Verbal and written communication
" Cross-functional/distributed team collaboration
" Ambiguity tolerance
" Ownership and accountability
" Technical documentation

Soft Skills Desired
" Mentoring junior engineers
" Technical leadership and design reviews
" Stakeholder management and vendor collaboration
" Continuous improvement
" Global team experience

Disqualifiers (Red Flags)
" No hands-on backend development
" Limited API, integration, or distributed systems experience
" Front-end only experience
" No Agile/Scrum experience
" Cannot contribute to cloud-native service development or troubleshooting

Key Responsibilities
" Identify cloud capabilities and benchmark industry adaptation
" Assess Kubernetes fit
" Review ICS/ACT technologies (Remote Services, Minestar)
" Challenge solutions and drive alternative architecture options
" Partner with GIS, Security, and other teams on solution design
" Evaluate and benchmark AI coding platforms
" Define AI best practices, standards, governance, and adoption frameworks
" Identify and execute AI pilot initiatives (Atlas, Physical AI, enterprise)
" Design agentic, spec-driven, and autonomous development workflows
" Measure developer productivity, quality, SDLC efficiency, and outcomes
" Create reference architectures, implementation patterns, and guidance for AI-native development
" Integrate AI into development lifecycle with architects, managers, product, and platform teams
" Assess security, compliance, and governance for AI coding tools
" Mentor teams on AI tools and modern engineering practices
" Drive velocity, quality, technical debt reduction, and developer experience improvements
" Collaborate with vendors, partners, and stakeholders on emerging capabilities and best practices
" Contribute to engineering strategy, roadmap, technology selection, and long-term AI transformation
" Individual contributor
" Collaborate with: Engineering Directors, Managers, Principal Engineers, Architects, Technical Leads
" Work across: Atlas, Physical AI, Autonomy Services, enterprise engineering
" Partner with: Product Owners, Product Managers, business stakeholders
" Partner with: DevOps, Platform Engineering, Cybersecurity, Enterprise Architecture
" Engage with vendors and AI platform providers
" Lead workshops, architecture discussions, PoCs, enablement activities
" Mentor engineers and technical leads
" Present recommendations, findings, pilot results, roadmaps to senior leadership