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
The ideal candidate will have deep experience designing high-complexity conversational AI and chatbot solutions, including agentic AI, multi-agent architectures, and agent-to-agent (A2A) interactions. This role requires a strong combination of AI architecture, machine learning, cloud, data, and hands-on technical expertise.
Key Responsibilities
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.
