Description

Responsibilities

• Lead the strategy, architecture, and technical design of enterprise AI, Generative AI, and Agentic AI solutions.
• Architect sophisticated conversational AI and 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 (GCP), 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.

Qualifications

• Required
  • 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 and agents with enterprise systems, APIs, tools, and data.
  • Deep experience with Google Cloud Platform (GCP) and building scalable AI/ML solutions in the GCP 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

Key Skills

Education

Bachelor's degree