QE GenAI Architect
3 days ago
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Key Responsibilities
- Architect end-to-end
GenAI-powered QE frameworks
integrating LLMs, autonomous testing agents, and intelligent validation techniques. - Develop
AI-driven test generation, self-healing scripts
, and automated defect classification engines. - Lead QE strategy across large enterprise programs, ensuring scalability, performance, and accuracy of automation systems.
- Partner with product, engineering, and data science teams to embed
AI/ML into SDLC
and create continuous quality pipelines. - Evaluate emerging LLMs, vector databases, RAG workflows, and MLOps platforms for QE optimization.
- Build reusable, modular accelerators for
AI-Augmented Regression, API validation, UI automation, and data intelligence
. - Mentor QE teams on GenAI adoption, automation best practices, and architectural governance.
- Drive innovation, solutioning, workshops, and customer presentations as a QE thought leader.
Must-Have Skills
- 14+ years of QE experience
with at least
3–5 years in GenAI/LLM-based automation
. - Expertise in Python, Java, or JavaScript with hands-on experience building automation frameworks.
- Strong understanding of
LLMs (GPT, Claude, Llama, etc.), vector stores (FAISS, Pinecone), RAG pipelines
, embeddings, and prompt engineering. - Proven experience with
AI-driven test generation, autonomous test agents, and intelligent defect prediction
. - Hands-on experience with
Selenium, Playwright, API automation
, and CI/CD test orchestration. - Deep familiarity with cloud platforms (
AWS, Azure, or GCP
) and container ecosystems (
Docker/Kubernetes
). - Strong architectural mindset with the ability to design scalable automation platforms.
Good-to-Have Skills
- Experience in
MLOps
, data pipelines, or AI model evaluation frameworks. - Knowledge of
GenAI observability, model drift detection, and guardrails
. - Experience with
Performance Engineering
, Chaos Testing, or Security Testing automation. - Prior consulting or customer-facing architecture leadership roles.
- Experience building reusable AI accelerators for enterprise teams.
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