Lead AI Software Engineer/Architect - 350

Position’s Contributions to Work Group:

Seeking a Principal AI Software Engineering Lead to drive adoption of AI-powered software engineering platforms and agentic development capabilities across enterprise engineering teams.

Primary focus is AI-assisted software development, engineering productivity transformation, developer tooling strategy, and autonomous engineering workflows leveraging platforms such as Cursor, Claude Code, GitHub Copilot, and emerging AI coding agents.

This role will establish AI engineering standards, evaluation frameworks, governance, and best practices while partnering with engineering teams to accelerate software delivery, improve quality, and enhance developer productivity.

Strong experience in software architecture, cloud-native platforms, backend engineering, distributed systems, and AI-assisted development workflows is required.

Key focus areas:

· AI Coding Platforms (Cursor, Claude Code, GitHub Copilot, etc.)

· Agentic Software Development & Specification-Driven Development

· Engineering Productivity & Developer Experience

· Software Architecture & Technical Leadership

· AI Evaluation, Governance & Adoption Strategy

· Cloud-Native Platforms, APIs & Distributed Systems

· Software Quality, Security & SDLC Modernization

Typical Task Breakdown

· Evaluate and benchmark AI software engineering platforms including Cursor, Claude Code, GitHub Copilot, and emerging agentic development tools.

· Define and implement AI-assisted software engineering best practices, standards, governance, and adoption frameworks.

· Partner with engineering teams to identify and execute high-value AI pilot initiatives across Atlas, Physical AI, and enterprise software development.

· Design and promote agentic development workflows, specification-driven development, and autonomous engineering practices.

· Measure and analyze developer productivity, software quality, SDLC efficiency, and engineering outcomes using data-driven metrics.

· Create reference architectures, implementation patterns, and engineering guidance for AI-native software development.

· Work closely with software architects, engineering managers, product teams, and platform teams to integrate AI capabilities into the development lifecycle.

· Evaluate security, compliance, and governance considerations related to AI coding tools and developer workflows.

· Mentor engineering teams on effective use of AI-powered development tools and modern software engineering practices.

· Drive continuous improvement initiatives focused on accelerating delivery velocity, improving code quality, reducing technical debt, and enhancing developer experience.

· Collaborate with vendors, strategic partners, and internal stakeholders to assess emerging AI engineering capabilities and industry best practices.

· Contribute to engineering strategy, roadmap planning, technology selection, and long-term AI transformation initiatives across Client.

Interaction with Team

· Partner closely with Engineering Directors, Engineering Managers, Principal Engineers, Architects, and Technical Leads to define and execute AI software engineering strategy.

· Collaborate with software development teams across Atlas, Physical AI, Autonomy Services, and enterprise engineering organizations to evaluate, pilot, and scale AI-assisted development capabilities.

· Work with Product Owners, Product Managers, and business stakeholders to identify high-value engineering productivity opportunities and prioritize adoption initiatives.

· Partner with DevOps, Platform Engineering, Cybersecurity, and Enterprise Architecture teams to establish governance, security, compliance, and operational standards for AI coding platforms.

· Engage with engineering leadership to define success metrics, measure outcomes, and communicate the impact of AI-assisted software development initiatives.

· Collaborate with external vendors and strategic partners, including AI platform providers, to evaluate emerging technologies and influence product direction.

· Lead technical workshops, architecture discussions, proof-of-concepts, and engineering enablement activities across multiple organizations.

· Mentor engineers and technical leaders on AI-native development practices, agentic workflows, and modern software engineering techniques.

· Present recommendations, findings, pilot results, and strategic roadmaps to senior leadership and executive stakeholders.

Team Structure

· Individual contributor, work with different stakeholders across enterprise

Work environment:

Office

Education & Experience Required:

- Years of experience: 5

- Degree requirement: Masters or Bachelor’s degree in Computer Science, Software Engineering

- Masters degree in Computer Science, Software Engineering with 3 Yrs of Exp

- Associate’s degree with 9 year’s experience

- Do you accept internships as job experience: No

Top 3 Skills

· AI-Assisted Software Engineering & Developer Productivity

· Backend/Platform Engineering (Java, Python, APIs, Cloud)

· Technical Leadership, Architecture & Engineering Transformation

Additional Technical Skills

(Required)

· Cursor, Claude Code, GitHub Copilot or similar AI coding tools

· Java/Spring Boot and Python development

· Distributed systems, APIs, cloud-native platforms

· Software architecture and engineering best practices

· Prompt engineering and agentic development workflows

· Engineering metrics and productivity measurement

(Desired)

· Spec-driven development

· Robotics, Physical AI, Simulation or Digital Twin exposure

· AWS/Azure cloud platforms

· Developer experience platforms and DevOps

Soft Skills

(Required)

· Strong problem-solving and analytical skills

· Ability to work effectively in agile scrum teams

· Strong verbal and written communication skills

· Collaboration across distributed and cross-functional teams

· Ability to work in ambiguous and evolving environments

· Ownership mindset with accountability for delivery

· Strong technical documentation skills

(Desired)

· Experience mentoring junior engineers

· Technical leadership and design review experience

· Stakeholder management and vendor collaboration

· Continuous improvement mindset

· Experience working with global teams