QE Architect

QUALITY ENGINEERING ARCHITECT

Own the quality engineering strategy and architecture for Claritev's application portfolio, with a mandate to modernize how quality is built, verified, and continuously assured through AI. This role designs and builds AI-powered testing agents and intelligent automation frameworks that plan, generate, execute, and analyze tests across the SDLC - while also acting as the organization's authority on TDD/BDD practices, regression, end-to-end, and performance testing strategy. The Quality Engineering Architect partners with engineering, product, and QA leadership to shift quality left, reduce manual test burden through AI agents, and coach teams toward sustainable, high-confidence testing practices.

AI-Driven Quality Engineering & Automation

1. Design, build, and maintain AI agents that automate test case generation, test data creation, self-healing test maintenance, defect triage, and root-cause analysis across the QA lifecycle.

2. Evaluate, select, and integrate AI/ML-based testing tools and frameworks (e.g., LLM-driven test generation, intelligent visual validation, predictive defect analytics) into the existing toolchain.

3. Define the architecture and governance model for AI-assisted QE - including prompt/agent design standards, guardrails for AI-generated test artifacts, and human-in-the-loop review processes.

4. Build reusable, agentic frameworks that plug into CI/CD pipelines to enable continuous, autonomous quality feedback (build-time, pre-release, and post-release).

Test Strategy & Architecture (Regression, End-to-End, Performance)

1. Architect the enterprise approach to regression testing, including scope optimization (risk-based test selection), automation coverage, and AI-assisted regression suite maintenance.

2. Design end-to-end testing solutions that validate complex, cross-system workflows spanning UI, API, services, and data layers, ensuring traceability from requirements through release.

3. Own the performance testing strategy - load, stress, scalability, and endurance testing - defining tooling, environments, benchmarks, and SLAs in partnership with engineering and infrastructure teams.

4. Establish test environment and test data strategies (including synthetic/AI-generated data) that support reliable, repeatable execution across regression, E2E, and performance suites.

Testing SDLC Ownership, TDD/BDD, and Coaching

1. Act as the organization's subject-matter expert on Test-Driven Development (TDD) and Behavior-Driven Development (BDD), embedding quality practices from requirements and design through code and deployment.

2. Coach and mentor engineers, SQA staff, and product teams on writing effective unit tests, BDD specifications (e.g., Gherkin/Cucumber), and test-first development habits.

3. Partner with Application Development, Product, and Business Analysis during technical design and story refinement to embed testability, TDD/BDD acceptance criteria, and quality gates from the start of the SDLC.

4. Champion and lead SQA/QE process improvement initiatives, including shift-left practices, quality metrics/dashboards, and maturity assessments across teams.

Leadership, Collaboration & Governance

1. Provide technical leadership and architectural direction to SQA/QE engineers on automation design, tool usage, and best practices - without direct people-management responsibility (architect/individual-contributor-at-scale role).

2. Collaborate, coordinate, and communicate across engineering, product, DevOps, and business stakeholders to align quality strategy with delivery goals.

3. Analyze test and quality metrics (coverage, defect trends, AI-agent performance/accuracy) to determine release readiness and continuously improve the quality engineering practice.

4. Prepare and present quality architecture roadmaps, test strategy documents, and results reporting to engineering and business leadership.

5. Ensure compliance with HIPAA regulations and requirements.

6. Demonstrate commitment to the Company's core values.

7. Please note due to the exposure of PHI sensitive data - this role is considered to be a High Risk Role.

8. The position responsibilities outlined above are in no way to be construed as all encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.

The incumbent operates with significant latitude and autonomy, exercising expert judgment on quality architecture, AI-driven tooling, and test strategy across the enterprise application portfolio. This is a senior individual-contributor / architect-level role: the incumbent sets technical direction and best practices for quality engineering broadly, and is looked to as the authority on AI-enabled testing, TDD/BDD, and large-scale regression/E2E/performance test architecture. Matters of significant business or technical impact are discussed consultatively with engineering and business leadership to determine the appropriate path forward. The incumbent may lead cross-functional initiatives and mentor engineers and SQA staff, without formal people-management responsibility.

• Minimum Bachelor's Degree in Computer Science, Engineering, or related field, or 10+ years of equivalent IT/quality engineering experience

• Minimum 8-10 years of experience in software quality assurance/quality engineering, including demonstrated experience architecting test solutions at scale

• Proven, hands-on experience designing and building AI agents or AI-powered tooling for software testing (e.g., automated test generation, self-healing automation, intelligent defect analysis)

• Strong working knowledge of LLM-based tools and frameworks and how to apply them responsibly within a QE context (prompt design, guardrails, human review)

• Deep expertise in TDD and BDD methodologies and associated tooling (e.g., JUnit/NUnit/pytest, Cucumber, SpecFlow, Gherkin), with a track record of coaching engineers and teams on these practices

• Expert-level experience designing and implementing regression, end-to-end, and performance/load testing strategies and frameworks (e.g., Selenium, Playwright, Cypress, JMeter, k6, LoadRunner)

• Strong knowledge of the full Software Development Life Cycle (SDLC) from a quality engineering standpoint, and of process improvement methodologies (e.g., Agile, DevOps, shift-left practices)

• Strong knowledge of SQL queries, PL/SQL, and working with Oracle (or comparable) databases

• Experience integrating automated and AI-driven testing into CI/CD pipelines

• Required licensures, professional certifications, and/or Board certifications as applicable

• Knowledge of business direction, trends, and impact of key business and IT initiatives

• Knowledge of medical terminology; knowledge of the health care industry a plus

• Strong communication (verbal, written, listening), coaching, analytical, and decision-making skills, with the ability to influence engineering practices across teams without direct authority

• Ability to analyze complex data (including AI-agent output and quality metrics) and arrive at logical, defensible conclusions

• Ability to assess the ramifications of issues and prioritize based on business and technical impact

• Ability to effectively present architecture, strategy, and results to groups of managers, engineers, and business stakeholders

• Ability to evaluate production and pre-production situations and determine appropriate, risk-informed courses of action

• Ability to mentor and coach engineers and SQA staff on testing practices and tools, without formal people-management responsibility

• Ability to operate effectively in a dynamic, fast-changing technology environment

• Ability to travel a few times per year

• Proficiency with MS Office/Project and relevant project management and test management software