1. Hands-on experience shipping production-ready generative AI applications at scale serving real
users
2. Strong understanding of LLM/ Agent internals: context window management, MCP Servers, tool
calling loops, prompt and context engineering, architecture trade-offs etc.
3. Proven experience with LangGraph, CrewAI, Semantic Kernel or similar agentic frameworks to
design complex multi-agent architectures and low code/ no code tools like Agent Kit, Copilot
Studio
4. Experience building sophisticated RAG and GraphRAG pipelines with vector databases and
knowledge graph-based retrieval
5. Practical experience leveraging coding agents (Claude Code, Codex) for spec-based rapid
development with tools like spec-kit
6. Production cloud experience deploying and scaling AI applications on AWS, GCP, or Azure
7. Proven track record mentoring junior and senior developers with strong technical communication
skills
Key Responsibilities
Design and architect scalable agentic AI systems
Build production-ready GenAI applications with focus on reliability and performance
Develop and optimize RAG/GraphRAG pipelines
Lead technical implementation and establish best practices
Mentor engineering teams on AI/ML architecture and implementation
Qualifications
8-12 years software engineering experience
2+ years hands-on experience with LLMs and generative AI
Bachelors/Masters in Computer Science, AI/ML, or equivalent experience
Technical Stack: Python, LangGraph/CrewAI, OpenAI/Claude/Gemini APIs, Vector DBs, Cloud platforms
(AWS/GCP/Azure), Graph Databases