AI Tools & Testing Architect

We are seeking a highly experienced AI Tools & Testing Architect with deep, hands-on expertise in designing, implementing, and scaling AI-driven solutions across software engineering—particularly in testing, quality engineering, and SDLC optimization. You will act as a technical architect and AI evangelist, guiding organizations in selecting the right AI tools, defining adoption frameworks, and embedding AI responsibly into engineering workflows.

Benefits:

• Onsite
• Competitive salary
• Opportunity for advancement

AI Tools & Testing Architect
Dallas, TX Onsite
Long-Term Duraiton

We are seeking a highly experienced AI Tools & Testing Architect with deep, hands-on expertise in designing, implementing, and scaling AI-driven solutions across software engineering—particularly in testing, quality engineering, and SDLC optimization.
This role combines technical architecture, strategic advisory, and hands-on enablement, helping engineering and QA teams effectively adopt AI to improve productivity, quality, and time-to-market.

You will act as a technical architect and AI evangelist, guiding organizations in selecting the right AI tools, defining adoption frameworks, and embedding AI responsibly into engineering workflows.

Key Responsibilities
AI Architecture & Implementation
• Architect, design, and implement AI-driven solutions across:
  Software testing and QA
  Quality engineering
  Broader software engineering workflows
• Design scalable, secure, and reusable AI reference architectures.
 
AI for Testing & Quality Engineering
• Define and lead AI adoption frameworks for testing use cases, including:
  Automated test case generation and optimization
  Test data generation, synthesis, and masking
  Defect prediction, anomaly detection, and root-cause analysis
  Intelligent test execution, prioritization, and coverage optimization
 
Tooling & Platform Strategy
• Evaluate, select, and recommend AI tools, platforms, and vendors, including:
  LLMs, agents, copilots
  AI-powered test automation tools
  Internal and external AI platforms
• Optimize AI tool integration for performance, cost, and reliability.

Engineering Enablement & Collaboration
• Collaborate with Engineering, QA, DevOps, Security, and Leadership teams to embed AI across the SDLC.
• Enable teams with:
  Best practices
  Design patterns
  Reference implementations
• Conduct workshops, demos, and enablement sessions.

Governance & Responsible AI
• Establish AI governance, security, and responsible AI guidelines
• Ensure compliance with enterprise security, data privacy, and ethical AI standards.

Mentorship & Technical Leadership
• Act as a technical mentor and advisor
• Guide teams and stakeholders (technical and non-technical) on AI adoption strategies.

Required Skills & Experience
• Strong hands-on experience with AI/ML and Generative AI, including:
              Large Language Models (LLMs)
                Prompt engineering
                  AI agents
                    Embeddings and vector search
                    Retrieval-Augmented Generation (RAG)
                       • Proven experience designing scalable AI architectures
• Deep understanding of:
          Software testing methodologies
            QA processes
            Test automation frameworks
• Experience integrating AI into:
            CI/CD pipelines
            DevOps and MLOps workflows
           
 • Familiarity with cloud-based AI platforms and APIs:
          AWS
            Azure
            GCP
• Strong ability to translate business problems into AI-driven technical solutions
• Excellent communication and stakeholder management skills

Nice to Have
• Experience with AI governance, security, and compliance
• Prior role as:
        AI Architect
          Solution Architect
            Principal Engineer
• Experience implementing AI in enterprise-scale environments
• Certifications in:
        Cloud platforms
        AI/ML
          Architecture frameworks

Success Criteria
• Demonstrated impact in: 
      Improving testing efficiency 
        Enhancing software quality 
        Reducing time-to-market using AI 
• Delivery of clear, reusable AI reference architectures and best practices 
• High adoption, engagement, and satisfaction across engineering and QA teams