AI Testing Architect

Select Minds•Dallas, TX

1d•$120,000 - $135,000•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

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