Lead Software Engineer - Quality Assurance / Test Automation / Agentic Systems
On-sitePlano, Texas, United States
Job Summary
Build and operate enterprise-scale automation and an agentic, human-gated testing ecosystem that accelerates delivery while protecting quality and auditability. Set and run journey quality strategy to convert product intent into risk-based acceptance criteria, measurable outcomes, and clear entry/exit standards. Engineer partner-facing integration validation by implementing contract tests, stubs, and automated suites that emulate partner behaviors and isolate dependency risk. Improve reliability of the testing system by reducing flakiness and time-to-diagnosis through stability patterns and clear quarantine rules. Own the defect prevention loop to lead reproduction, triage, fix validation, and prevention, feeding failure patterns back into coverage and automation improvements. Report journey health with clarity by providing executive-ready readiness reporting that explains risk, coverage, and release posture in plain language. Lead communities of practice to drive awareness of new technologies and foster a culture of diversity, inclusion, and respect.
Required Qualifications
- Formal training or certification on software/quality engineering, software development in testing automation concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s) and framework(s) (i.e., Java, BDD suites - cucumber/gherkin, SQL, Rest API, Jira, etc.)
- Proficiency hands on engineering, automation depth and continuous delivery methods (UI automation - playwright/selenium), along with experience in API testing with a track record of building stable suites (selectors/fixtures, reliability patterns, deterministic data, flake control)
- Understands enterprise testing ecosystems with familiarity and ability to standardize across frameworks such as FiTest+ and/or other harnesses used by the organization (alongside modern OSS stacks)
- Build and operationalize an agentic testing ecosystem to design, implement, and run a human-gated agent framework that accelerates test authoring, selection, execution, and triage—connecting agents to real tools (frameworks, CI/CD, APIs, data sources) while enforcing guardrails for correctness, determinism, security, and auditability
- Release readiness & governance with entry/exit criteria, evidence-based sign-off, and strong partnership in Release Management in a CD operating model - along with curate and version a trusted test catalog, continuously measuring agent “fitness” (precision, drift, false positives/negatives), and ensure outputs remain release-grade across environments
- Establish and maintain strong partnership across Product/Engineering/Operations and other lines of business, with executive-ready reporting and risk articulation
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Experience building and operating MCP Servers/clients to connect LLM agents to tools (APIs, databases, and internal services)
Desired Qualifications
- Demonstrate ability to implement human-in-the-loop guardrails to test review standards, signed/versioned catalogs, drift controls, and coverage integrity mechanisms
- Demonstrate proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Experience designing and implementing agentic/LLM-assisted pipelines for continuous test selection, execution, and reporting across environments, operating MCP Servers/clients to connect LLM agents to tools (APIs, databases, and internal services)
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