Applied AI SDET

Job Title:

Applied AI SDET

Location:

Dallas, TX

Duration: / Term:
Contract

Experience Desired: 5+ Years

Job Description:

AI-generated code introduces quality risks that traditional QA processes were not designed to catch. Subtle logic errors, incomplete edge case handling, and systematically misapplied patterns can pass human review when reviewers are moving fast.

Your role goes beyond building a safety net - you will design and implement AI-driven quality engineering processes, tools, and agents that make quality intrinsic to the delivery pipeline rather than a gate at the end of it. You will feed intelligence back into the practice so the pipeline continuously improves, and you will build the automation infrastructure that allows the pod to ship with confidence at a pace that manual QA cannot sustain.

What You Will Do

• Own the Test Strategy: Define and maintain the end-to-end test strategy for AI-generated code across unit, integration, contract, and E2E layers, calibrated to the risks introduced by agentic delivery.
• Build AI-Driven Quality Agents: Design and implement AI-powered quality agents and tooling - using Claude or equivalent - that automate test generation, coverage gap analysis, regression triage, and defect pattern detection within the CI/CD pipeline.
• Validate AI-Generated Tests: Critically assess Claude-generated unit and integration tests for completeness, correctness, and meaningful coverage. Identify gaps, redundancies, and tests that pass without actually validating behavior.
• Build and Maintain Automation Suites: Design, implement, and own automated test suites that run as quality gates in the CI/CD pipeline, including regression safety nets that protect the codebase from agentic regressions.
• Test Containers & Isolated Test Environments: Design and manage containerized, isolated test environments using Test Containers and Docker to ensure backend service tests run against production-equivalent dependencies - databases, message queues, and third-party service stubs - without shared state or environment bleed.
• Synthetic Data Engineering: Design and maintain synthetic data strategies that produce realistic, consistent, and constraint-safe test data for backend service testing,
• ensuring coverage of edge cases, boundary conditions, and stateful workflows without reliance on production data.
• Establish AI Quality Engineering Processes: Define and document repeatable quality engineering processes tailored to the AI-DLC model - covering how tests are generated, reviewed, validated, and evolved alongside AI-generated features.
• Identify AI Failure Patterns: Detect and document systematic patterns in Claude's output quality - recurring anti-patterns, common security misses, or edge cases Claude consistently overlooks - and feed these back to the AI Solution Owner to improve specs and prompt context.
• Partner on Spec Quality: Review feature specifications before agentic generation begins, flagging ambiguities or missing acceptance criteria that will produce untestable or unverifiable output.
• Own Your Delivery: Take full responsibility for test coverage and quality sign-off on every feature delivered by the pod, from spec handoff through production deployment and post-deploy verification.

Must Have

Experience:

• SDET / QA Engineering Proficiency: 5+ years in a software development engineer in test or senior QA role, with a track record of building and owning test automation in an enterprise software delivery context.
• Python & FastAPI Testing:
  Proven experience testing Python/FastAPI backend services including async endpoint testing, dependency injection overrides, and integration test patterns with pytest and httpx.
• Type Script & React Testing:
  Experience writing and maintaining tests for Type Script/React frontends using React Testing Library, Jest, and component-level testing patterns.
• Node.js Testing:
  Proven experience testing Node.js backend services using Jest, Supertest, or Mocha - including middleware testing, async handler validation, and integration testing of REST and Graph
  
  QL APIs built with Express, Fastify, or NestJS.
• Test Containers & Isolated Environments:
  Hands-on experience with Test Containers (Python or Java) to spin up isolated, containerized dependencies - Postgre
  
  SQL, Redis, Kafka, or equivalent - for reliable, repeatable integration tests with zero shared state.
• Synthetic Data Engineering:
  Demonstrated ability to design synthetic data pipelines that generate realistic, constraint-safe test data for stateful backend services, covering edge cases and boundary conditions without using production data.
• API & Contract Testing:
  Proven experience with REST and Graph
  
  QL API testing, contract testing (Pact or equivalent), and validating service boundaries in microservices or distributed systems.
• AI-Augmented Development
  
  Experience:
  
  Demonstrable experience using AI coding agents (Claude Code, Git Hub Copilot, Cursor, or equivalent) as a delivery tool, including the ability to critically evaluate and extend AI-generated test…