AI Testing Architect

Benefits

• Competitive salary
• Health insurance
• Opportunity for advancement

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

• 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 Dev Ops 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

• 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, Git Hub Actions, Azure Dev Ops)
• Experience with at least one cloud platform (AWS, Azure, or GCP)
• Strong understanding of software testing principles, QA processes, and SDLC

• Experience with Lang Chain or Llama Index
• 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

Languages: Python (primary), Java or JavaScript (optional)

Testing: Selenium, Playwright, Cypress, PyTest, TestNG

AI/GenAI: OpenAI APIs, Lang Chain or Llama Index, embeddings, RAG

Data: Vector databases (Pinecone, FAISS, Weaviate)

Cloud: AWS, Azure, or GCP

CI/CD: Jenkins, Git Hub Actions, Azure Dev Ops

• 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