1. Hands-on experience shipping production-ready generative AI applications at scale serving realusers2. Strong understanding of LLM/ Agent internals: context window management, MCP Servers, toolcalling loops, prompt and context engineering, architecture trade-offs etc.3. Proven experience with LangGraph, CrewAI, Semantic Kernel or similar agentic frameworks todesign complex multi-agent architectures and low code/ no code tools like Agent Kit, CopilotStudio4. Experience building sophisticated RAG and GraphRAG pipelines with vector databases andknowledge graph-based retrieval5. Practical experience leveraging coding agents (Claude Code, Codex) for spec-based rapiddevelopment with tools like spec-kit6. Production cloud experience deploying and scaling AI applications on AWS, GCP, or Azure7. Proven track record mentoring junior and senior developers with strong technical communicationskillsKey ResponsibilitiesDesign and architect scalable agentic AI systemsBuild production-ready GenAI applications with focus on reliability and performanceDevelop and optimize RAG/GraphRAG pipelinesLead technical implementation and establish best practicesMentor engineering teams on AI/ML architecture and implementationQualifications8-12 years software engineering experience2+ years hands-on experience with LLMs and generative AIBachelors/Masters in Computer Science, AI/ML, or equivalent experienceTechnical Stack: Python, LangGraph/CrewAI, OpenAI/Claude/Gemini APIs, Vector DBs, Cloud platforms(AWS/GCP/Azure), Graph Databases