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Software testing Jobs in Dallas, TX
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Software testing • dallas tx
- Promoted
AI Tools & Testing Architect
Select Minds LLCDallas, TX, US- Promoted
- New!
Testing Analyst
VirtualVocationsIrving, Texas, United States- Promoted
Manager, Compliance Testing
ScotiabankDallas, TX, US- Promoted
Golf Simulator Testing Specialist
Uneekor, IncDallas, TX, United States- Promoted
Construction Materials Testing Technician I
Building & Earth Sciences, Inc.Irving, TX, United States- Promoted
Construction Materials Testing Laboratory Technician
KleinfelderIrving, TX, United States- Promoted
Construction Materials Testing Technician
Intertek USA, Inc.Dallas, TX, United States- Promoted
Construction Materials Testing Technician
CerterraDallas, TX, US- Promoted
Construction Materials Testing Technician II
Building & Earth SciencesDallas, TX, United States- Promoted
Construction Materials Testing Technician
Dallas StaffingDallas, TX, United States- Promoted
ERS NETA Testing Technician
Purple DriveDallas, TX, United StatesStaff Software Engineer - Testing Architect
GEICODallas, TX- Promoted
Hardness Testing Sales Rep
Babich & Assoc.Dallas, TX, US- Promoted
LVN Pre-Admissions Testing
Texas Institute for SurgeryDallas, TX, United States- Promoted
Electrical Testing Technician (NETA)
Techpro Power Group (All)Dallas, TX, USSoftware Development Engineer in Test (Infrastructure Testing - IaC)
DTCCDallas, TX, USTesting Engineering Specialist
NTT DATADallas, TX, United States- Promoted
Construction Materials Testing Technician
DIHO Consulting USA, LLC.Dallas, TX, US- Promoted
K12 Testing Monitor
Assessment Intervention ManagementDallas, TX, United States- chief medical officer (from $ 119,438 to $ 343,000 year)
- hospitalist (from $ 35,000 to $ 300,000 year)
- orthodontist (from $ 25,000 to $ 295,000 year)
- live in nanny (from $ 25,106 to $ 265,688 year)
- reservoir engineer (from $ 160,000 to $ 250,000 year)
- chief of staff (from $ 129,900 to $ 240,000 year)
- general dentist (from $ 50,000 to $ 240,000 year)
- corporate development (from $ 119,912 to $ 240,000 year)
- physician (from $ 80,544 to $ 237,500 year)
- psychiatrist (from $ 120,000 to $ 237,500 year)
- Los Angeles, CA (from $ 92,500 to $ 221,568 year)
- San Diego, CA (from $ 70,720 to $ 183,972 year)
- New York, NY (from $ 88,200 to $ 152,000 year)
- Seattle, WA (from $ 117,500 to $ 145,425 year)
- Atlanta, GA (from $ 105,000 to $ 143,800 year)
- Phoenix, AZ (from $ 92,050 to $ 143,401 year)
- Chicago, IL (from $ 60,600 to $ 136,260 year)
- Kent, WA (from $ 117,500 to $ 132,500 year)
- Pittsburgh, PA (from $ 80,000 to $ 126,225 year)
- Austin, TX (from $ 78,000 to $ 122,600 year)
The average salary range is between $ 80,006 and $ 152,000 year , with the average salary hovering around $ 107,500 year .
Related searches
AI Tools & Testing Architect
Select Minds LLCDallas, TX, US- Full-time
Job Description
Job Description
Benefits :
- Competitive salary
- Opportunity for advancement
AI Tools & Testing Architect
Dallas, TX Onsite
Long-Term Duraiton
Role Overview
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 engineeringparticularly 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