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) #LI-DV1