QA Automation Architect/Lead

Golden Technology; Blue Ash (OH); On-site; USD 180,000 - 240,000; Full time

Job summary

Golden Technology in Ohio seeks an AI-driven Quality Engineering leader to guide cross-functional teams, review designs, and set quality standards across complex software initiatives.

You will shape roadmaps, drive reliable delivery, and mentor senior engineers while promoting responsible AI adoption and governance in engineering practices.

Qualifications

• 10+ years in software engineering, quality engineering, platform engineering, or related technical disciplines.
• Experience leading large-scale technical initiatives without direct authority.
• Strong understanding of modern software delivery practices and distributed systems.
• Deep understanding of automation frameworks and CI/CD practices.
• Knowledge of release quality, risk assessment, defect prevention, and production readiness.
• Demonstrated experience applying AI tools to engineering workflows.
• Ability to evaluate AI opportunities beyond simple code generation.
• Understanding of strengths, limitations, risks, and governance considerations associated with AI-assisted engineering.
• Must be able to review code effectively.
• Review automation frameworks and technical designs.
• Guide engineering teams without becoming the primary implementer.
• Lead initiatives that span multiple engineering teams and organizational boundaries.
• Translate broad business objectives into actionable roadmaps.
• Identify systemic causes behind recurring quality, release, or operational challenges.
• Drive sustainable solutions rather than temporary fixes.
• Define quality engineering strategies, standards, and architectures.
• Guide teams on test strategy, automation, release quality, observability, test environments, and risk management.
• Review designs, frameworks, and implementation approaches.
• Influence architecture decisions through a quality and reliability lens.
• Identify and implement opportunities to leverage AI across the software development lifecycle.
• Evaluate emerging AI capabilities and determine practical applications.
• Improve engineering productivity, test effectiveness, and delivery speed through AI-assisted workflows.
• Promote responsible and measurable AI adoption.
• Investigate complex technology, process, and organizational issues.
• Challenge assumptions and drive root-cause analysis.
• Create clarity where none exists.
• Partner with Engineering, Product, Architecture, SRE, Release Management, and Quality Engineering leaders.
• Mentor senior engineers and technical leads.
• Lead through influence rather than authority.
• Elevate engineering practices across the organization.

Responsibilities

• Lead initiatives that span multiple engineering teams and organizational boundaries.
• Translate broad business objectives into actionable roadmaps.
• Identify systemic causes behind recurring quality, release, or operational challenges.
• Drive sustainable solutions rather than temporary fixes.
• Define quality engineering strategies, standards, and architectures.
• Guide teams on test strategy, automation, release quality, observability, test environments, and risk management.
• Review designs, frameworks, and implementation approaches.
• Influence architecture decisions through a quality and reliability lens.
• Identify and implement opportunities to leverage AI across the software development lifecycle.
• Evaluate emerging AI capabilities and determine practical applications.
• Improve engineering productivity, test effectiveness, and delivery speed through AI-assisted workflows.
• Promote responsible and measurable AI adoption.
• Investigate complex technology, process, and organizational issues.
• Challenge assumptions and drive root-cause analysis.
• Create clarity where none exists.
• Partner with Engineering, Product, Architecture, SRE, Release Management, and Quality Engineering leaders.
• Mentor senior engineers and technical leads.
• Lead through influence rather than authority.
• Elevate engineering practices across the organization.

Skills

Technical Leadership

Distributed Systems

Software Delivery Practices

Influence without Authority

Job description

Required Experience

Technical Leadership

• 10+ years in software engineering, quality engineering, platform engineering, or related technical disciplines.
• Experience leading large-scale technical initiatives without direct authority.
• Strong understanding of modern software delivery practices and distributed systems.

Quality Engineering Expertise

• Deep understanding of automation frameworks and CI/CD practices.
• Knowledge of release quality, risk assessment, defect prevention, and production readiness.

AI Fluency

• Demonstrated experience applying AI tools to engineering workflows.
• Ability to evaluate AI opportunities beyond simple code generation.
• Understanding of strengths, limitations, risks, and governance considerations associated with AI-assisted engineering.

Technical Depth

Must be able to:

• Review code effectively.
• Review automation frameworks and technical designs.
• Guide engineering teams without becoming the primary implementer.

Key Responsibilities

Drive Complex Cross-Functional Outcomes

• Lead initiatives that span multiple engineering teams and organizational boundaries.
• Translate broad business objectives into actionable roadmaps.
• Identify systemic causes behind recurring quality, release, or operational challenges.
• Drive sustainable solutions rather than temporary fixes.

Technical Leadership

• Define quality engineering strategies, standards, and architectures.
• Guide teams on test strategy, automation, release quality, observability, test environments, and risk management.
• Review designs, frameworks, and implementation approaches.
• Influence architecture decisions through a quality and reliability lens.

AI-Enabled Quality Engineering

• Identify and implement opportunities to leverage AI across the software development lifecycle.
• Evaluate emerging AI capabilities and determine practical applications.
• Improve engineering productivity, test effectiveness, and delivery speed through AI-assisted workflows.
• Promote responsible and measurable AI adoption.

Solve Ambiguous Problems

• Investigate complex technology, process, and organizational issues.
• Challenge assumptions and drive root-cause analysis.
• Create clarity where none exists.

Organizational Influence

• Partner with Engineering, Product, Architecture, SRE, Release Management, and Quality Engineering leaders.
• Mentor senior engineers and technical leads.
• Lead through influence rather than authority.
• Elevate engineering practices across the organization.