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AI Testing Architect

Select Minds LLC

Dallas, TX

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JOB DETAILS
SKILLS
Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), Automation, Cloud Computing, Computer Programming, Continuous Deployment/Delivery, Continuous Integration, Cost Control, DevOps, GCP (Good Clinical Practices), GitHub, Health Insurance, Information/Data Security (InfoSec), Java, JavaScript, Jenkins, Machine Tool, Metrics, Microsoft Windows Azure, Performance Tuning/Optimization, Production Systems, Pytest, Python Programming/Scripting Language, Quality Assurance, Quality Assurance Methodology, Quality Engineering, Regression Testing, Selenium, Software Development Lifecycle (SDLC), Software Engineering, Software Testing, Technical Leadership, Test Automation, Test Case, Test Data, Test Design, Testing, Time Management, Use Cases
LOCATION
Dallas, TX
POSTED
30+ days ago
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 Auth : Must be authorized to work in the U.S.     
Domain: Enterprise AI / Agentic AI / AWS Bedrock
Compensation: Competitive, commensurate with experience

We are hiring a senior AI Testing Architect to design and implement AI-driven solutions across software testing and quality engineering. This role focuses on applying Generative AI to improve test coverage, reduce cycle time, and modernize QA practices.

You will work hands-on with engineering and QA teams while also guiding tooling decisions and adoption approaches. This is a high-impact individual contributor role with ownership of architecture, implementation, and practical AI adoption across testing workflows.

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

About the Company

S

Select Minds LLC

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