Lead Quality Engineer With AI

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

1. 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.
2. Cross-Functional Influence & Problem Solving - Able to work across multiple teams, solve ambiguous problems, identify root causes, and drive change without having direct authority.
3. 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.