Enterprise Architect - Level 10

$ 180 k - 200 k per annum

1. Enterprise Quality Engineering Transformation

• Lead large-scale transformation initiatives to evolve traditional Quality Assurance into modern Quality Engineering.
• Assess the organization''s existing QA/QE ecosystem, automation maturity, tools, frameworks, processes, and engineering practices.
• Define and implement a target-state enterprise Quality Engineering architecture.
• Establish enterprise-wide standards, principles, methodologies, and governance for automated testing.
• Develop modernization roadmaps covering people, processes, technology, tooling, architecture, and operating models.
• Identify opportunities to improve software quality, release velocity, test coverage, automation effectiveness, and engineering productivity.
• Establish quality engineering practices across multiple development teams, applications, platforms, and technology stacks.
• Drive adEligible to workion of shift-left and continuous quality practices across the SDLC.
• Define measurable QE KPIs, automation metrics, quality gates, and engineering productivity metrics.

2. AI-Driven Quality Engineering

• Lead the adEligible to workion of AI-driven Quality Engineering across the Software Development Lifecycle.

• Design AI-powered testing workflows that improve test generation, execution, analysis, maintenance, and defect detection.

• Evaluate and implement AI-assisted engineering tools and platforms.

• Develop strategies for using AI coding assistants such as:

• GitHub Copilot

• Claude

• Google Gemini

• Other enterprise AI coding assistants and agentic development tools

• Establish enterprise standards for the responsible use of AI-generated test automation code.

• Review, debug, validate, Eligible to workimize, and refactor automation code generated by AI assistants.

• Ensure AI-generated code meets enterprise requirements for:

• Maintainability

• Reliability

• Security

• Performance

• Scalability

• Coding standards

• Reusability

• Testability

• Design workflows using AI agents and intelligent automation to improve engineering productivity.

• Investigate opportunities for autonomous test creation, test Eligible to workimization, defect analysis, test data generation, and regression analysis.

• Establish validation mechanisms for AI-generated code and AI-generated tests.

3. Spec-Driven Design / Development

• Define and implement enterprise-level Spec-Driven Design (SDD) methodologies.

• Establish standards for converting business and technical specifications into executable quality criteria and automated tests.

• Integrate SDD with BDD, automation frameworks, CI/CD pipelines, and software development processes.

• Develop reusable specifications, scenarios, acceptance criteria, and automated validation patterns.

• Establish traceability between:

• Business requirements

• Technical specifications

• User stories

• Acceptance criteria

• Automated tests

• Defects

• Release validation

• Promote specification-first development practices across engineering teams.

• Ensure SDD practices support both human-developed and AI-assisted software development.

4. Deterministic Quality Gates and AI Code Validation

• Define enterprise quality gates for software developed by both engineers and AI coding agents.
• Establish deterministic validation criteria before code can progress through development, testing, staging, and production en

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Disclaimer: Diverse Lynx LLC is an Equal Opportunity Employer. All applicants and employees are evaluated without discrimination, based solely on their qualifications, ability, competence and performance. This email and its attachments may contain confidential or proprietary information and is intended only for the recipient(s). If you received this message in error, please disregard it and notify the sender. If you no longer wish to receive our communications, you may unsubscribe at any time.

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