AI Testing Architect

Benefits Competitive salary Health insurance Opportunity for advancement Job Overview Job Title: AI Testing Architect (GenAI / QA Automation) Work Type: Full-Time/Contract Location: Dallas, Texas Onsite Interview Mode: Virtual + In-Person (depends) Work Authorization: Must be authorized to work in the U.S. Domain: Enterprise AI / Agentic AI / AWS Bedrock Compensation: Competitive, commensurate with experience Key Responsibilities Design and implement AI-driven solutions for test automation, test data generation, and defect detection Build and deploy LLM-based workflows (e.g., test case generation, RAG-based validation, anomaly detection) Evaluate, select, and integrate AI tools and frameworks for QA and SDLC use cases Develop reusable architecture patterns for AI-enabled testing across teams Integrate AI solutions into CI/CD pipelines and existing engineering workflows Collaborate with Engineering, QA, and DevOps teams to drive practical AI adoption Optimize performance, cost, and reliability of AI-based solutions in production Provide technical guidance and hands‐on support to engineers adopting AI tools Contribute to lightweight AI governance practices, including data handling, security, and responsible usage Required Qualifications 8+ years of experience in software engineering, QA automation, or test architecture 3+ years of hands‐on experience with AI/ML or Generative AI in production environments Strong experience with test automation frameworks (Selenium, Playwright, Cypress, PyTest, TestNG) Strong programming skills in Python Experience building or integrating LLM-based solutions (prompting, RAG, embeddings, vector search) Experience integrating solutions into CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps) Experience with at least one cloud platform (AWS, Azure, or GCP) Strong understanding of software testing principles, QA processes, and SDLC Preferred Qualifications Experience with LangChain or LlamaIndex Experience with vector databases (Pinecone, FAISS, Weaviate) Exposure to MLOps practices and model lifecycle management Experience with AI governance, security, or compliance frameworks Prior experience as an AI Architect, Solution Architect, or Principal Engineer Experience working in enterprise‐scale environments Technical Stack Languages: Python (primary), Java or JavaScript (optional) Testing: Selenium, Playwright, Cypress, PyTest, TestNG AI/GenAI: OpenAI APIs, LangChain or LlamaIndex, embeddings, RAG Data: Vector databases (Pinecone, FAISS, Weaviate) Cloud: AWS, Azure, or GCP CI/CD: Jenkins, GitHub Actions, Azure DevOps Success Metrics Reduce regression testing cycle time through AI-driven automation Improve test coverage and defect detection using AI-generated test assets Deliver reusable AI architecture patterns adopted across teams Drive measurable adoption of AI tools within engineering and QA workflows #J-18808-Ljbffr