AI-Native Software Engineering Director
S
SparkrockEnterprise Software
This role is open to applicants from any country. We hire globally.Full-TimeDirector
SalaryOTE $100,000/year USD
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Job Details
- Experience
- 8+ years
- Required Skills
- CI/CDDevOpsSoftware EngineeringChange Management
Requirements
- Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent practical experience.
- 8+ years of hands-on software engineering experience delivering production software systems.
- Strong background in modern software development practices and production-grade systems.
- Practical experience using AI-assisted development tools, coding agents, AI-enabled IDEs, or agentic software development workflows.
- Experience evaluating and rolling out AI engineering tools, coding agents, and developer productivity platforms.
- Experience leading engineering transformation, developer productivity, or quality engineering initiatives.
- Experience designing, executing, and scaling experiments that improve engineering outcomes.
- Experience driving the adoption of new engineering practices and coaching teams through organizational change.
- Ability to design human-AI workflows that maintain security, reliability, and human accountability.
- Strong analytical and data-driven decision-making skills with the ability to define meaningful metrics.
- Exceptional change leadership and communication skills to influence across all levels of the organization.
Responsibilities
- Design, execute, and measure AI-Native software development and quality engineering experiments.
- Identify engineering bottlenecks where AI-Native workflows can improve productivity, quality, speed, developer experience, or release confidence.
- Evaluate emerging AI engineering tools, coding agents, AI-enabled development environments, and developer productivity platforms.
- Develop and institutionalize AI-Native development, testing, review, documentation, refactoring, debugging, and delivery practices.
- Define and maintain engineering quality bars, operating standards, usage guardrails, and best practices for AI-assisted development.
- Coach engineers and engineering leaders to maximize effectiveness through AI-assisted development, agentic workflows, and human-AI collaboration.
- Establish balanced metrics and measurement frameworks for engineering productivity, quality, and business impact.
- Partner with engineering, product, QA, security, and DevOps leadership to align AI-Native transformation efforts with business priorities.
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