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.