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Remote in Argentina, & 4 others
AI Solution Engineering& 3 others
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We're in search of a highly capable, hands-on Conversational AI Architect to design, construct, and deploy cutting-edge agentic AI platforms. In this capacity, you'll act as the critical bridge linking state-of-the-art Generative AI models with complex enterprise production environments. The scope of this work goes well beyond architectural design alone; you'll write production-grade code, coordinate multi-agent workflow orchestration through Gemini CX / Vertex AI Agent Builder, manage deterministic state machines using Dialogflow CX, and build secure cloud data pipelines built on the Advanced Agent Modeling (AAM) framework
Responsibilities
- Construct and deploy autonomous, results-driven multi-agent systems using Gemini CX and Vertex AI Agent Studio, elevating customer interactions beyond simple deflection into complete end-to-end resolution
- Oversee hybrid conversational flows by combining generative playbooks with precise state-machine management inside Dialogflow CX, making use of Pages, Parameters, Conditional Routing, and Transition Routes
- Build, refine, and sustain Retrieval-Augmented Generation (RAG) pipelines through Vertex AI Search, anchoring agents securely to unstructured enterprise knowledge sources
- Engineer and deploy secure backend integrations, connectors, and webhooks via Cloud Run, Cloud Functions, and the Model Context Protocol (MCP), tying conversational platforms into enterprise CRMs, ERPs, and internal data systems
- Ensure enterprise-level security and compliance by implementing Model Armor safeguards, configuring VPC Service Controls (VPC-SC), and applying strict IAM least-privilege standards
- Build out telemetry pipelines moving raw conversation transcripts from Dialogflow into BigQuery, applying CCAI Insights to enable large-scale error-taxonomy review, intent health monitoring, and containment rate tracking
Requirements
- Build, refine, and sustain Retrieval-Augmented Generation (RAG) pipelines through Vertex AI Search, anchoring agents securely to unstructured enterprise knowledge sources
- Engineer and deploy secure backend integrations, connectors, and webhooks via Cloud Run, Cloud Functions, and the Model Context Protocol (MCP), tying conversational platforms into enterprise CRMs, ERPs, and internal data systems
- Ensure enterprise-level security and compliance by implementing Model Armor safeguards, configuring VPC Service Controls (VPC-SC), and applying strict IAM least-privilege standards
- Build out telemetry pipelines moving raw conversation transcripts from Dialogflow into BigQuery, applying CCAI Insights to enable large-scale error-taxonomy review, intent health monitoring, and containment rate tracking
- Requirements
- A minimum of 5 years of applicable hands-on background
- At least 2 years spent in Senior, Lead, Manager, or Owner-level capacities
- Demonstrated history successfully managing engagements involving teams of 5 or more full-time staff
- Deep, practical experience spanning the Google Cloud AI ecosystem, including Gemini CX, Google Cloud Contact Center AI (CCAI), and the Advanced Agent Modeling (AAM) framework
- Strong capability in conversation engineering, covering Dialogflow CX, Speech-to-Text (STT) tuning (phrase hints, audio profiles), Text-to-Speech (TTS), and telephony/SIP trunking setup
- Solid programming skill in Python or Node.js, with experience building custom automation tooling or working with Google Cloud wrapper libraries such as dfcx-scrapi considered a strong plus
- Reliable background working with GCP infrastructure and DevOps tooling, including Cloud Run, Cloud Functions, BigQuery, Looker Studio, and Infrastructure-as-Code practices through Terraform
- Familiarity with cloud security principles, data anonymization approaches, and maintaining model training boundaries within enterprise tenant constraints
- Demonstrated history guiding generative AI agents from pilot or MVP stages through to full-scale, live deployment serving millions of users
- Fluent English communication skills at a B2 level or higher
Nice to have
- Possession of a Google Cloud Professional Cloud Architect or Professional Data Engineer certification
- Completion of Google Cloud's official Agentic AI on Google Cloud or Gemini Enterprise for Customer Experience (GECX) training programs
- Background deploying large-scale customer service solutions within Financial Services, Telecom, Retail, or Healthcare sectors