We're seeking an accomplished, implementation-focused Conversational AI Architect to design, build, and launch sophisticated agentic AI platforms. This role positions you as the vital link connecting advanced Generative AI models to complex, production-grade enterprise systems. The responsibilities reach well beyond high-level design work; you'll author production-ready code, direct multi-agent workflow coordination via Gemini CX / Vertex AI Agent Builder, administer deterministic state machines through Dialogflow CX, and construct secure cloud data pipelines using the Advanced Agent Modeling (AAM) framework Responsibilities Assemble and roll out autonomous, outcome-driven multi-agent systems through Gemini CX and Vertex AI Agent Studio, pushing customer interactions beyond basic deflection toward comprehensive end-to-end resolution Administer hybrid conversational pathways by blending generative playbooks with exact state-machine control within Dialogflow CX, applying Pages, Parameters, Conditional Routing, and Transition Routes Construct, improve, and maintain Retrieval-Augmented Generation (RAG) pipelines leveraging Vertex AI Search, securely connecting agents to unstructured enterprise knowledge bases Design and launch secure backend integrations, connectors, and webhooks using Cloud Run, Cloud Functions, and the Model Context Protocol (MCP), bridging conversational systems with enterprise CRMs, ERPs, and internal databases Maintain enterprise-grade security and regulatory compliance by implementing Model Armor protections, establishing VPC Service Controls (VPC-SC), and applying rigorous IAM least-privilege rules Establish telemetry pipelines channeling raw conversation transcripts from Dialogflow into BigQuery, utilizing CCAI Insights to support extensive error-taxonomy evaluation, intent health assessment, and containment rate analysis Requirements At least 5 years of relevant practical experience A minimum of 2 years functioning in Senior, Lead, Manager, or Owner-level roles Proven record overseeing engagements for teams of 5 or more full-time employees Extensive hands-on background across the Google Cloud AI ecosystem, encompassing Gemini CX, Google Cloud Contact Center AI (CCAI), and the Advanced Agent Modeling (AAM) framework Demonstrated expertise in conversation engineering, including Dialogflow CX, Speech-to-Text (STT) tuning (phrase hints, audio profiles), Text-to-Speech (TTS), and telephony/SIP trunking configuration Strong coding proficiency in Python or Node.js, with background developing custom automation scripts or utilizing Google Cloud wrapper libraries such as dfcx-scrapi viewed as a significant advantage Consistent background working with GCP infrastructure and DevOps resources, including Cloud Run, Cloud Functions, BigQuery, Looker Studio, and Infrastructure-as-Code methods via Terraform Working knowledge of cloud security fundamentals, data anonymization techniques, and maintaining model training isolation within enterprise tenant boundaries Clear history transitioning generative AI agents from pilot or MVP phases into fully scaled, live production serving millions of end users Fluent English communication skills at a B2 level or higher Nice to have Holding a Google Cloud Professional Cloud Architect or Professional Data Engineer certification Having completed Google Cloud's official Agentic AI on Google Cloud or Gemini Enterprise for Customer Experience (GECX) training tracks Background implementing large-scale customer service solutions across Financial Services, Telecom, Retail, or Healthcare industries
