• Lead the strategy, architecture, and technical design of enterprise AI, Generative AI, and Agentic AI solutions.
• Architect sophisticated conversational AI/chatbot platforms capable of supporting complex workflows, reasoning, orchestration, and enterprise integrations.
• Design agentic and multi-agent AI architectures, including agent-to-agent transactions, communication, orchestration, tool usage, and workflow execution.
• Design and implement Retrieval-Augmented Generation (RAG) architectures leveraging enterprise structured and unstructured data.
• Architect solutions utilizing knowledge graphs to improve contextual understanding, reasoning, relationships, and information retrieval.
• Develop and guide machine learning and Generative AI solutions across enterprise use cases.
• Design AI architectures leveraging Model Context Protocol (MCP) to securely connect AI agents and models with enterprise tools, systems, APIs, and data sources.
• Architect and deploy AI/ML solutions within Google Cloud Platform (Google Cloud Platform), leveraging appropriate cloud-native AI, data, compute, and integration services.
• Lead architecture discussions and technical strategy sessions with senior business and technology stakeholders.
• Translate complex business requirements and technical documentation into clear AI solution designs, architecture patterns, roadmaps, and implementation strategies.
• Evaluate AI technologies, models, frameworks, and architectural approaches and provide recommendations based on business and technical requirements.
• Establish best practices around AI scalability, security, governance, performance, reliability, and responsible AI.
• Provide technical leadership and architectural guidance to engineering, data science, machine learning, and platform teams.
• Develop prototypes and reference implementations using Python to validate architectural concepts and AI capabilities.
Required Qualifications
• Extensive experience as an AI Solutions Architect, AI Architect, ML Architect, or similar senior technical architecture role.
• Strong experience architecting complex enterprise chatbot and conversational AI solutions.
• Deep understanding of Agentic AI and multi-agent systems, including agent-to-agent (A2A) communication, orchestration, reasoning, tool calling, and autonomous workflows.
• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Strong experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
• Experience with knowledge graphs, semantic relationships, graph-based retrieval, and/or knowledge-driven AI architectures.
• Strong foundation in machine learning concepts, architectures, and production ML solutions.
• Experience with Model Context Protocol (MCP) and integrating AI applications/agents with enterprise systems, APIs, tools, and data.
• Deep experience with Google Cloud Platform (Google Cloud Platform) and building scalable AI/ML solutions in the Google Cloud Platform ecosystem.
• Strong Python development experience for AI/ML applications, integrations, prototyping, and solution development.
• Experience working with structured and unstructured enterprise data, including document ingestion, extraction, translation, summarization, and intelligent document processing.
• Strong understanding of APIs, microservices, cloud architecture, data integration, security, and enterprise application architecture.
• Ability to communicate complex AI concepts to both technical and non-technical stakeholders.
• Demonstrated ability to drive AI strategy, influence architectural decisions, and lead technical conversations across multiple teams.
Core Technical Skills
Must Have:
Google Cloud Platform • Generative AI • LLMs • Agentic AI • Multi-Agent / A2A Systems • Complex Chatbots / Conversational AI • RAG • Knowledge Graphs • Machine Learning • MCP • Python • Enterprise AI Architecture • Document AI / Document Processing
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