Description

Key Responsibilities AI Architecture Leadership: Define the enterprise architecture, reference patterns, and technology roadmap for AI-powered Quality Engineering solutions. Agentic AI and RAG: Design reusable AI-agent patterns and a RAG Knowledge Fabric integrating Jira, Confluence, Git repositories, CI/CD platforms, test assets, ServiceNow, and approved enterprise data sources. Orchestration and Integration: Design multi-agent orchestration and integration services supporting requirements review, code quality, test preparation, test execution, QE intelligence, and release assurance. Governance and Responsible AI: Establish Human-in-the-Loop controls, security guardrails, explainability, confidence scoring, model evaluation, observability, auditability, and cost controls. Stakeholder and Technical Leadership: Lead architecture reviews, prioritize high-value use cases, guide onsite and offshore teams, and communicate solution decisions to business and engineering leadership. Scale and Adoption: Define the maturity path from supervised to semi-autonomous and autonomous agents while ensuring scalability, reliability, performance, and adoption. Required Qualifications • 12+ years of technology experience, including 5+ years in AI/ML, Generative AI, or intelligent automation. • Strong experience with LLM applications, Agentic AI, multi-agent systems, RAG, embeddings, vector search, prompt engineering, and AI evaluation. • Experience with enterprise AI architecture, microservices, APIs, event-driven architecture, containers, and cloud-native deployment. • Hands-on knowledge of Claude, Gemini, Codex, Azure OpenAI, or equivalent enterprise LLM platforms. • Proficiency in Python and working knowledge of Java and/or TypeScript, REST APIs, FastAPI, Git, CI/CD, Docker, and Kubernetes. • Strong knowledge of responsible AI, security, privacy, model-risk controls, monitoring, and production-support practices. • Strong customer-facing communication, architecture presentation, and cross-functional leadership skills. Preferred Qualifications • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. • Experience in Retail, Supply Chain, Logistics, Distribution, or a related business domain. • Relevant AI, cloud, architecture, software engineering, or Quality Engineering certifications

Key Skills

Generative AI, Agentic AI, Multi-Agent Architecture, RAG, Knowledge Fabric, Vector Search, Prompt Engineering, AI Evaluation, Responsible AI, Python, FastAPI, APIs, Microservices, Docker, Kubernetes, CI/CD, Jira, Confluence, GitHub/GitLab, Azure DevOps, ServiceNow. Success Measures

Key Skills

Education

Any Gradute