GenAI Architect
- Architect and oversee the implementation of RAG pipelines, knowledge retrieval, and context grounded generation.
- Design multi agent ecosystems enabling collaboration through Agents at Agents and MCP Multi Context Protocol frameworks.
- Establish technical blueprints, reference architectures, and reusable design patterns for AI and LLM based solutions.
- Serve as the technical authority across AIML, data, and integration layers guiding AI Engineers, ML Engineers, and Data Engineers.
- Drive architecture reviews, design validations, and compliance with Google AI best practices and enterprise standards.
- Implement AI observability frameworks for performance tracking, model evaluation, and continuous improvement.
- Oversee end-to-end solution deployment on Google Cloud Vertex AI, App Engine, BigQuery, Cloud Functions, Looker
- Lead architecture modernization and migration of legacy components into agent-based microservice frameworks.
- Conduct technical workshops, POCs, and architecture deep dives with customer and partner teams.
- 10 years of experience in software architecture with 3 years in AIML and LLM based system design.
- Proven expertise in Google LLM ecosystem Gemini Models, Vertex AI, ADK, and Agent Builder.
- Strong understanding of RAG frameworks vector search and contextual knowledge retrieval BigQuery Vector Search FAISS Pinecone
- Hands on design experience with A@A and MCP for multi-agent orchestration and communication.
- Proficiency in Python, TypeScript, or Go, with experience in API design, orchestration, and system integration.
- Deep understanding of cloud native architecture, microservices, event driven design, and AI security governance
- Strong grounding in promp engineering context management and evaluation methodologies for LLMs
- Certified Google Cloud Professional Architect or Vertex AI Specialist.
- Experience integrating LangChain LlamaIndex or AgentOps with Google ADK
- Familiarity with MLOps or AgentOps pipelines for model and agent lifecycle management
- experience in architecting RAG based enterprise copilots knowledge assistants or automated decision systems