AI Solutions Architect – Quality Strategy

🚀 AI Solutions Architect – Quality Strategy
📍 Remote | Anywhere in the U.S.

We're looking for a strategic leader to drive our Quality-at-Source vision and transform how quality is embedded across engineering teams.

🔹 Lead quality standards, tooling, and best practices across the organization
🔹 Champion Test-Driven Development (TDD) and Behavior-Driven Development (BDD)
🔹 Drive the shift from reactive QA to a proactive, AI-powered development lifecycle
🔹 Define verification & validation (V&V) standards for AI-driven applications
🔹 Partner with engineering leaders to build a culture of quality and continuous improvement

✅ Experience in software quality strategy, test automation, and engineering leadership
✅ Strong background in TDD, BDD, and modern software delivery practices
✅ Knowledge of quality frameworks for AI/ML applications

🌟 Join us and help shape the future of AI-driven quality engineering.

• Strategic Engineering Leadership
   
  • Process Transformation: Lead the design and conversion of legacy quality assurance processes into a modern, continuous improvement framework with shift-left testing and validation.
  • TDD/BDD Implementation: Establish, evangelize, and implement TDD and BDD methodologies across the entire application portfolio to ensure code is testable and requirements are executable.
  • V&V Governance: Own the ultimate validation and verification of the SDLC, ensuring that quality is baked into the CI/CD pipeline and local development environments.
     
• AI Validation Focus
   
  • AI Verification: Design and implement concrete validation pipelines for AI-assisted development outputs, including:
     
    • Prompt engineering standards — Establish reusable prompt templates and guardrails for agentic code generation, with version-controlled prompt libraries.
    • Output validation gates — Build automated checks that evaluate AI-generated code against security, style, and correctness baselines before it enters the review cycle.
    • Non-deterministic testing frameworks — Develop assertion strategies for AI outputs where exact results vary (e.g., confidence-scored evaluations, boundary testing, regression suites against known-good outputs).
    • Human-in-the-loop checkpoints — Define which categories of AI output require manual review (UX decisions, business logic, security-sensitive code) and build the workflow tooling to surface them efficiently.
       
  • Intelligent Tooling: Own an evaluation-driven roadmap for AI developer tools. Each tool adoption must include a measurable hypothesis (e.g., “AI-assisted test generation reduces test authoring time by 40% within 90 days”) with a defined pilot, measurement period, and go/no-go criteria before org-wide rollout.
  • Agentic Engineering Coaching: Develop and deliver training programs that teach development teams to treat AI agents as junior developers — validating outputs, writing effective prompts, and designing workflows where AI acceleration doesn’t bypass quality gates.
• Culture & Influence
   
  • Diplomatic Change Management: Use persuasion and diplomacy to bridge gaps between product, engineering, and operations, moving the organization toward a collaborative "Quality First" mindset.
  • Thought Leadership: Leading at the company as a pioneer in AI-driven quality engineering and software validation in general.
     
• Product Owner Partnership
   
  • TDD and BDD are only as effective as the requirements they validate. This role must have an explicit mandate to partner with — and push back on — product ownership:
  • Acceptance criteria quality standards — Define what “good enough to build against” looks like. Work with product managers to ensure stories include testable acceptance criteria before engineering begins work.
  • Requirements readiness gate — Authority to flag and return insufficiently specified work to product before it enters a sprint. If requirements are garbage in, quality will be garbage out regardless of test automation.
  • BDD collaboration model — Establish a structured process where product, engineering, and QA co-author behavioral specifications (Given/When/Then) before development begins, ensuring shared understanding of “correct.”
     

• Advanced Degree in related field required
• Minimum of seven years of related experience is required.
• Prior management/supervisory experience is required.
• Technical Expertise
   
  • Development Roots: A strong background in software development (e.g., Java, Python, or C#, node ecosystem) with a genuine passion for the art of code verification.
  • SDLC Mastery: Deep experience in building and optimizing CI/CD pipelines and local developer workflows.
  • Methodology Expert: Proven track record of successfully deploying TDD and BDD at an enterprise scale.
  • Agentic Engineering: Strong experience in AI assisted development with an emphasis on building validation into the AI generation.
     
• Leadership Traits
   
  • Results-Oriented: A focus on metrics that matter (e.g., Lead Time, Change Failure Rate, and Mean Time to Recovery).
  • The "Diplomatic Architect": Ability to influence senior stakeholders and mentor junior engineers simultaneously.
  • Continuous Learner: Obsessed with the evolving landscape of AI and engineering tooling.
     
• ISTQB Test Manager or ASQ Certified Software Quality Engineer (CSQE). Preferred 
• Project Management Professional (PMP) or Certified Scrum Master (CSM). Preferred 
• AWS Certified Developer. (Preferred)

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