Role: Lead Quality Engineer With AI
Location: Blue Ash, OH, 45242
Employment Type: Contract
Contract or Contract to Hire: Contract to Hire preferred, Contract only resources will also be considered. Possibility to extend.
Project: Next Generation Point of Sale (NGPOS) - Client's modern retail checkout/platform environment, built to support store transactions and related retail workflows using a more modern, cloud-enabled architecture.
About the Role
The Principal Quality Engineering Lead is a senior individual contributor responsible for driving transformational quality engineering outcomes across product, engineering, release, and operations teams.
This role requires a unique blend of technical depth, systems thinking, organizational influence, and relentless curiosity. The individual will lead complex initiatives spanning quality engineering, test architecture, release readiness, environment strategy, AI-enabled engineering practices, and software delivery governance.
Success in this role is measured by business outcomes, not activity metrics. The ideal candidate can take an ambiguous problem, define a strategy, align stakeholders, and drive execution with minimal oversight.
This role does not have direct people management responsibilities but is expected to operate at a leadership level through influence, coaching, and technical leadership.
Required Qualifications
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
- Experience defining test strategies across multiple test levels.
- 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.
- Evaluate architectural tradeoffs.
- Guide engineering teams without becoming the primary implementer.
Tech Stack / Environment:
- React, Java/Spring microservices, MQTT, Azure/ISC, automated CI/CD, contract testing, mutation testing, synthetic monitoring, and automated quality gates.
- Specific stack experience is less important than strong modern engineering / QE fundamentals and the ability to quickly work across technologies.
Top 3 Skills
- Technical Quality Engineering Leadership - Strong background in modern QE, automation, CI/CD, release quality, and the SDLC, with enough technical depth to review code, frameworks, and architecture.
- Cross-Functional Influence & Problem Solving - Able to work across multiple teams, solve ambiguous problems, identify root causes, and drive change without having direct authority.
- AI Fluency in Engineering - Comfortable using AI to evaluate ideas, solve technical problems, improve testing and engineering practices, and work effectively across different technology stacks.
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.
- Build consensus among diverse stakeholders.
- 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.
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