AI-Native Software Engineering Director
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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 Engineering
Requirements
- Bachelor's degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field, or equivalent practical experience.
- 8+ years of hands-on software engineering experience delivering production software systems.
- Strong hands-on software engineering background with experience in modern software development practices and production-grade systems.
- Practical experience using AI-assisted development tools, coding agents, AI-enabled IDEs, AI-powered testing, AI-supported code review, or agentic software development workflows in real engineering environments.
- Experience evaluating and rolling out AI engineering tools, coding agents, test generation tools, code review assistants, documentation assistants, or developer productivity platforms.
- Experience leading engineering transformation, engineering excellence, developer productivity, quality engineering, platform engineering, technical enablement, or software development process improvement initiatives.
- Experience designing, executing, measuring, and scaling experiments that improve engineering productivity, quality, developer experience, or delivery outcomes.
- Experience driving the adoption of new engineering practices across multiple teams or organizations.
- Strong understanding of modern software engineering, software quality engineering, testing strategies, automation, CI/CD, DevOps, cloud-native development, observability, security, and developer productivity practices.
- Strong analytical and data-driven decision-making capabilities, including the ability to define meaningful metrics, establish baselines, interpret results, and avoid vanity metrics.
- Exceptional coaching, mentoring, facilitation, and change leadership skills.
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, test generation tools, code review assistants, documentation tools, 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, workflow templates, and best practices for AI-assisted software development.
- Create AI-Native quality engineering practices that improve test automation, regression prevention, validation, code review, quality gates, and production readiness.
- Establish balanced metrics and measurement frameworks for engineering productivity, quality, cycle time, developer experience, adoption, and business impact.
- Coach engineers and engineering leaders to maximize effectiveness through AI-assisted development, agentic workflows, quality engineering, and human-AI collaboration.
- Drive organization-wide adoption of proven AI-Native engineering practices through coaching, enablement, influence, measurement, and continuous feedback loops.
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