AI Testing Architect
Select Minds LLC, Dallas, TX, United States
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
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