Lead AI QA Engineer

About SkySpecs:
At SkySpecs, our mission is to simplify renewable asset management so that less can do more for the planet. SkySpecs is helping to make this possible by automating the operations and maintenance of wind farms using advanced robotics paired with our custom-built asset performance management software, Horizon.

SkySpecs launched the world’s first completely autonomous blade inspection product in 2016 with a custom-designed drone system. Since then, SkySpecs has inspected over 90% of the US wind turbines and expanded globally, becoming the world leader in understanding the health of turbine blades. Identifying issues with turbine blades is only the first step. In 2019, SkySpecs launched Horizon, a platform for SkySpecs to offer a multi-layered solution for customers that includes: data collection, wind turbine blade engineering expertise, and a place for all stakeholders to collaborate to manage and analyze massive amounts of data, spot trends, and create plans for high-cost repair campaigns. In 2021, SkySpecs acquired two companies specializing in wind turbine drivetrain monitoring and financial management, further expanding our asset management portfolio. Ultimately, this will help reduce the cost and risk of operations for the industry.

What will you be getting into?

Join a high-impact, cross-functional team of Product Managers, Engineers, and Data Experts building a next-generation analytics and decision intelligence platform for the renewable energy industry.

We are looking for a Senior / Lead AI QA Engineer who will shape and scale our AI-first Quality Engineering practice. This is a strategic, hands-on leadership role focused on designing and implementing intelligent, agent-driven testing frameworks that elevate quality across the product lifecycle.

You will take ownership of architecting AI-powered test automation ecosystems, embedding AI agents within CI/CD pipelines, and driving continuous quality improvement across engineering teams. Beyond execution, you will mentor QA engineers, define testing strategy, and champion a quality-first mindset across the organization.

As a Lead AI QA Engineer, you will:

• Architect and implement Agentic AI Testing Solutions that autonomously generate, execute, optimize, and maintain test suites
• Lead the integration of AI agents into regression testing, defect clustering, root cause analysis, and coverage optimization
• Drive adoption of AI-powered IDEs to accelerate debugging, test generation, and code validation
• Establish scalable AI-driven quality frameworks across teams
• Evaluate and introduce next-generation AI testing tools and strategies

Quality Strategy & Execution:

• Define and own the overall QA strategy aligned with business and engineering goals
• Design scalable automation frameworks and intelligent test architectures
• Oversee exploratory, automated, performance, and security testing initiatives
• Monitor and report key quality metrics (defect density, open defect counts, test coverage, automation ROI)
• Ensure continuous quality validation throughout the SDLC

Leadership & Cross-Functional Collaboration:

• Lead and mentor QA engineers, promoting AI-first testing practices
• Collaborate with Engineering, Product, DevOps, and Leadership teams to define system requirements and quality standards
• Provide clear, structured feedback on requirements, technical designs, and architecture
• Drive quality ownership across teams

Requirements of the job:

• 5+ years of experience in QA / Quality Engineering
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• Deep hands-on experience with Agentic AI Testing Solutions (special focus required)
• Proven leadership experience in QA or Quality Engineering (Senior/Lead level)
• Strong experience integrating AI agents into automated testing and CI/CD pipelines
• Extensive experience with AI-powered IDEs and AI-assisted development workflows
• Significant experience with both white-box and black-box testing
• Strong automation framework design experience
• Solid knowledge of SQL and scripting
• Strong understanding of Agile/Scrum and modern DevOps practices

Bonus Points:

• Excellent written and verbal communication skills
• Ability to articulate complex technical quality concepts to both technical and non-technical stakeholders
• Strong documentation skills (test strategies, architectural decisions, QA roadmaps)
• Experience in influencing engineering culture and driving process improvements
• Experience leading distributed QA teams
• Experience with performance, security, and scalability testing
• Experience building AI-driven QA Centers of Excellence

Perks of the Job:

• Time Off: Generous leave policy—among the best in the industry.
  
• Global Team: Work with teammates across 5 countries and diverse backgrounds.
• Impact: Contribute to building the backbone of clean energy digital infrastructure.