Job Summary: The AI QE Architect will lead the design, development, and optimization of next-generation AI-powered quality engineering solutions and platforms. This role combines deep technical expertise in automation engineering with hands-on experience in LLMs, agentic AI frameworks, and enterprise-grade AI tooling. The architect will define strategy, design scalable frameworks, guide teams, and drive innovation across QE automation, AI agents, RAG pipelines, and MCP-enabled intelligent workflows. Key Responsibilities: Design, develop, and enhance GenAI-powered quality engineering solutions, AI agents, and autonomous workflows. Apply LLMs, prompt engineering, RAG, vector databases, and model evaluation techniques to quality engineering initiatives. Design and implement agentic AI solutions using frameworks such as LangGraph, AutoGen, and CrewAI. Implement MCP-driven, context-aware automation and CI/CD decision intelligence. Architect and maintain automation frameworks using Playwright and Selenium for UI testing. Develop API automation solutions using PyTest, Requests, and RestAssured. Design and maintain performance testing solutions using JMeter and Locust. Develop prompt-optimized, AI-generated test assets and validation mechanisms. Build data and embedding pipelines and optimize retrieval capabilities for RAG solutions. Implement CI/CD processes for ML models, including versioning, evaluation, and retraining workflows. Integrate automation pipelines using GitHub Actions, Azure DevOps, and Jenkins. Design scalable, secure, and governed AI and automation environments across AWS, Azure, and Google Cloud Platform. Provide technical leadership and mentor teams on AI adoption and automation engineering best practices. Collaborate with developers, SMEs, and product teams to define architecture, priorities, and technical roadmaps. Drive feature prioritization, quality strategy, and solution design across AI-powered QE initiatives. Lead defect triage, quality reviews, and compliance with QE and AI governance standards. Contribute across the full SDLC, including test strategy, design, execution, and analysis. Operate effectively within Agile/Scrum environments. Required Qualifications: Strong hands-on experience with LLMs, prompt engineering, RAG, vector databases, and model evaluation. Proficiency with LangChain, HuggingFace, Transformers, and OpenAI/Ollama APIs. Experience with agentic AI frameworks such as LangGraph, AutoGen, and CrewAI. Strong coding skills in Python, TypeScript, or Java. Extensive experience architecting and maintaining automation frameworks for UI, API, and performance testing. Experience with Playwright, Selenium, PyTest, Requests, RestAssured, JMeter, and/or Locust. Experience with PyTorch, TensorFlow, Scikit-Learn, and NLP/CV libraries such as NLTK, BART, or OpenCV. Experience building data and embedding pipelines and optimizing retrieval for RAG. Experience implementing CI/CD for ML models, including model versioning, evaluation, and retraining workflows. Strong understanding of AWS, Azure, and/or Google Cloud Platform architectures and AI/ML services. Experience integrating automation pipelines with GitHub Actions, Azure DevOps, and Jenkins. Strong understanding of AI/automation security, scalability, governance, and enterprise integration. Strong technical leadership, mentoring, communication, and collaboration skills. Experience working across the full software development lifecycle in Agile/Scrum environments. Education: Bachelors Degree
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: compun
- Position Id: MALDC5872267
- Posted 15 hours ago
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