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 GCP.
• 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 GCP 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.