Senior Software Quality Engineer

ZipLiens•Franklin, TN

22h

About The Position

Zipliens is a lien resolution company whose software helps personal injury law firms manage lien processes and helps injured people access settlement funds more quickly and accurately. The Senior Software Quality Engineer will lead quality for applications serving the legal industry as the engineering organization scales. Quality engineering is embedded in engineering teams and combines exploratory testing with automation. As the senior quality engineer, you will establish team standards for test coverage, merge and deployment gates, frameworks, tools, and risk-based testing. You will lead quality for medium to large features, including cross-team initiatives, and own risk analysis, test strategy, release criteria, and final sign-off. You will also develop evaluation practices for AI features, including LLM-driven extraction of structure from medical records and lien correspondence, and establish standards for reviewing AI-generated output and using agentic coding tools in testing.

Requirements

At least 5 years on a QA or software development team, with experience across a range of products and test approaches.
Expert-level skill in at least one of exploratory testing and test design, test automation and infrastructure, or AI/LLM evaluation, with solid proficiency in the other two.
Experience setting a team's testing methodology, standards, and framework choices, and gaining team adoption.
Significant experience building and owning automation in a modern framework such as Playwright, Cypress, pytest, or similar, including designing its CI/CD gating strategy.
Comfort working in code, reviewing test code, assessing changes for risk, and contributing to shared test infrastructure; the backend uses Python/Django and the frontend uses React.
Strong SQL and PostgreSQL skills, including verifying data produced by workflows as well as their UI output.
Hands-on experience evaluating LLM or ML features in production, including golden datasets, acceptance thresholds, calibrating LLM judges against human labels, and monitoring quality after release.
Experience using agentic coding tools for test work and understanding where their output requires human review.
Experience mentoring quality engineers or developers and raising standards on a growing team.
Excellent written and verbal communication skills for defining release criteria, explaining risk to non-technical stakeholders, and advocating for quality investment with engineering leadership.

Nice To Haves

Experience testing document processing or extraction from unstructured data, including defining acceptance thresholds.
Experience building or owning test infrastructure shared across multiple teams.
Experience testing third-party integrations, particularly when documentation is limited and contract tests, sandboxes, or recorded fixtures are needed.
Familiarity with AWS and Kubernetes; the role will work with the SRE on test environments and failure investigation but will not run the cluster.
Experience handling sensitive data such as PHI or working under HIPAA-style requirements.

Responsibilities

Lead quality for medium to large features, including cross-team initiatives; own risk analysis, test strategy, release criteria, and final sign-off.
Set, follow, and advocate for team testing standards and frameworks, including coverage expectations and what a passing build guarantees.
Own automation and CI/CD gating strategy, including which suites run at each stage, merge and deployment gates, and management of runtime and flakiness.
Own evaluation methodology for LLM features, including sampling, significance, rubric design, regression gates for prompt and model changes, production monitoring, drift alerts, and rerunning golden sets when vendors update models.
Set standards for validating AI-generated output in the product and in AI-generated tests and tooling.
Collaborate with the Product Owner to define acceptance and release criteria and act as the primary QA contact for cross-functional planning.
Evaluate and introduce new tools and AI capabilities for the team.
Mentor quality engineers and coach engineers in exploratory technique and risk-based testing.
Assess application designs for testability early, while changes are less costly.