Job Title: Principal AI Software Engineering Lead
Location: Irving, TX, 75038 (Onsite)
Duration: 26 months contract
**Preferred locals to TX.
**Only those lawfully authorized to work in the designated country associated with the position will be considered.
Must Have Skills/Attributes: Cloud, DevOps, Java, Python.
Experience Desired : AI-Assisted Software Engineering & Developer Productivity (7 yrs); Backend/Platform Engineering (7 yrs); Backend/Platform Engineering (7 yrs); Software Architecture & Technical Leadership (7 yrs); Software Architecture & Technical Leadership (7 yrs)
Required Minimum Education : Bachelor’s Degree.
Preferred Education : Master’s Degree.
JOB DESCRIPTION:
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.