Conversational AI Engineer – Google, GECX, Code Agents

• Design, deploy, and optimize next-generation customer-facing agentic workflows.
• Build and configure modular, hierarchical multi-agent networks using CX Agent Studio canvas and programmatic Code Agents.
• Write specialized backend logic, data transformation scripts, and custom Python or TypeScript tool callbacks.
• Run automated code-first developer workflows using uv environments and the cxas CLI, including structural and semantic linting.
• Author and maintain structured XML-like agent instructions with custom tool hooks and sub-agent routing.
• Integrate Code Agents with databases, product catalogs, CRMs, and CCaaS networks such as Genesys and Avaya via MCP or secure OpenAPI webhooks.
• Automate golden-scenario test cases and simulation evaluations for CI/CD, preventing hallucinations and conversational regressions before production synchronization.
• Bridge digital discovery through Google Search and Maps with customer operations across chat, voice, and retail/commerce touchpoints.

• Bachelor’s degree in computer science, engineering field, or equivalent practical experience.
• 3+ years of experience building conversational AI solutions, virtual agents, or programmatic AI workflows.
• Strong proficiency in Python or TypeScript/Node.js, with hands-on experience handling complex JSON payloads, RESTful APIs, and cloud-hosted webhooks.
• Experience utilizing Git-based developer workflows and command-line interfaces (CLIs) for software versioning and deployment.
• Hands-on experience building with Google’s GECX Code Agent tooling (e.g., cxas-scrapi workspace/SDK, Python-based agent foundries).
• Good technical understanding of Dialogflow CX, CX Agent Studio, or similar advanced, stateful conversational frameworks.
• Experience deploying streaming, low-latency audio-to-audio (A2A) voice agents and integrating with telephony (SIP/RTP) or CCaaS partner environments.
• Solid grasp of prompt engineering safety protocols, blocklists, and programmatic fallback behaviors (e.g., before_model_callback or after_tool_callback hooks).
• Experience with BigQuery or Google Cloud Logging to programmatically extract conversation traces, search text transcripts, and build data-driven performance metrics.