Job Description: Software Engineering Lead – AI-Augmented Software Development
Role Summary
We are seeking a Software Engineering Lead – AI-Augmented Software Development, who can drive the next generation of software engineering by combining strong technical depth with expertise in GenAI, agentic workflows, and AI-augmented software development. This is not a traditional engineering role. We are looking for someone who can design and implement AI-driven software engineering process, establish best practices, and lead how AI is used across the SDLC—not just write code faster. You are expected to use AI as a force multiplier across the entire SDLC, including design, coding, testing, debugging, documentation, and production support.
You will play a key role in defining how teams build, test, deploy, and operate software using AI, while ensuring quality, security, and scalability at an enterprise level.
Locations: San Fransisco, CA, Dallas, TX and Austin, TX
Key Responsibilities
- Define and implement AI-augmented software development process, including the use of GenAI tools, coding assistants, and agentic systems across development, testing, debugging, and documentation
- Design and operationalize agent-based software development processes, where multi-step engineering tasks (feature development, bug fixing, modernization) are executed through structured AI workflows
- Lead the design and development of scalable, secure software systems while actively contributing hands-on to architecture, coding, and critical problem-solving
- Establish standards, guardrails, and best practices for AI-assisted development, including prompt design, validation frameworks, security constraints, and quality benchmarks
- Break down complex engineering problems into structured, AI-executable tasks with clear context, constraints, and validation criteria, enabling consistent and scalable AI-assisted delivery
- Guide teams in effectively leveraging AI by reviewing and validating outputs, identifying hallucinations, security risks, incomplete implementations, and architectural gaps before production use
- Drive improvements in engineering productivity and outcomes, defining and tracking metrics such as cycle time, defect rates, test coverage, and automation levels
Required Qualifications
- 8+ years of strong software engineering experience with deep expertise in system design, APIs, distributed systems, and cloud-native architecture
- Hands-on experience with GenAI tools and platforms (e.g., GitHub Copilot, Cursor, OpenAI, Claude, Gemini, or similar), with a clear understanding of how to apply them effectively in software development workflows
- Demonstrated ability to design and guide AI-assisted development, not just use AI tools passively
- Proficiency in one or more languages such as Java, Python, JavaScript/TypeScript, or similar, with the ability to operate across the full stack when needed
- Strong experience with testing, CI/CD, DevOps practices, and production systems
- Ability to critically evaluate AI-generated outputs for correctness, security, and completeness
Preferred Qualifications
- Experience building or implementing agentic software development workflows or AI-driven automation systems
- Experience modernizing legacy applications using AI-assisted approaches
- Cloud experience (AWS, Azure, or GCP) and familiarity with enterprise-scale systems
- Exposure to governance, security, and compliance considerations in enterprise environments
- Prior experience mentoring engineers or leading technical initiatives
What Success Looks Like
In this role, success is not just measured by code delivered, but by how effectively you:
- Drive adoption of AI augment software development in a structured, measurable, and sustainable way
- Establish scalable AI-driven software development practices & Processes
- Enable teams to significantly improve software delivery speed and quality
- Reduce manual effort through automation and intelligent workflows
- Ensure all AI-assisted software development outputs meet enterprise-grade coding standards