GenAI Solution Architect

15+ years in software/solution architecture, proven experience as a Data Scientist or ML Engineer with exposure to agent-based AI systems. Design & Implement Agentic AI systems using agent frameworks (AutoGen, LangGraph, CrewAI, etc.) to build multi-agent and goal-oriented systems. Proficiency in Prompt Engineering, few-shot prompting, chain-of-thought reasoning, and prompt templates. Familiarity with cloud-native AI platforms from AWS, Azure, or GCP (e.g., Bedrock, Azure OpenAI, Vertex AI). Experience on AI for Engineering & working with AI Code Assist tools (e.g., Copilot, Windsurf, Cursor) Experience working with Vector databases & design & deploy RAG pipelines, MCP Servers & A2A Implement robust LLMOPs - for continuous integration, deployment, monitoring, logging, and troubleshooting mechanisms for GenAI applications. Develop and promote reusable architectural patterns, best practices, and governance frameworks for GenAI development. Lead the end-to-end architectural design of Generative AI applications, ensuring scalability, performance, security, cost-effectiveness, and maintainability. Proficiency in containerization technologies (Docker, Kubernetes) and CI/CD pipelines. Must have experience working with Microservice architecture using Spring Boot Rest APIs & know API Security, Versioning Must have experience designing CloudNative applications on any cloud such as AWS, Azure, GCP, Spring Cloud, PCF Programming proficiency in Python (preferred), and optionally Java/Node.js for integration. Collaborate with Data Scientists, Product Owners, and Business SMEs to translate business problems into AI-powered solutions. Exceptional communication and presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences