O pozici
Software engineering is undergoing its biggest transformation since Agile, Cloud, and DevOps. AI is changing how software is designed, built, tested, reviewed, documented, and delivered. The organizations that learn how to turn this shift into disciplined engineering practice will create a meaningful advantage in speed, quality, innovation, and talent leverage.
At Sparkrock, we help social benefit organizations—such as nonprofits, school boards, and government agencies—operate more effectively. Every day, tens of thousands of users rely on our platforms to manage critical financial and administrative workflows.
Sparkrock is looking for an AI-Native Software Engineering Director to lead that transformation. This is not a traditional software engineering leadership role focused on roadmap execution, release management, or managing a large reporting line. Your mission is to build the AI-Native Engineering Operating System for Sparkrock: the experiments, workflows, standards, metrics, guardrails, playbooks, and coaching systems that define how our engineering teams build software in the AI era.
Co budeš dělat
- 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.
- Analyze experiment results and recommend whether practices should be adopted, modified, scaled, or retired.
- Create playbooks, frameworks, operating models, and enablement materials that turn successful experiments into repeatable practices across the organization.
- 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.
- Define safe and responsible practices for AI-generated code, AI-assisted testing, tool usage, data exposure, IP protection, security, maintainability, and human review.
- Partner with engineering, product, QA, security, DevOps, platform, and executive leadership to align AI-Native transformation efforts with business priorities.
- Continuously improve software development, QA, automation, CI/CD, DevOps, cloud engineering, observability, security, and delivery processes through AI-Native approaches.
- Develop strategic recommendations for the future evolution of software engineering at Sparkrock.
