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You must be proficient in Python, utilizing object-oriented programming, modern type hinting, and asynchronous patterns.\nEnvironment & Dependency Management: Familiarity with modern Python package managers like uv, virtual environments (.venv), and pip.\nCommand Line Interface (CLI) Proficiency: Ability to navigate CLI tools, as well as general shell scripting\n Conversational AI / Agent Architecture:\nGenerative Agent Design: Shifting from legacy intent-based state machines to generative, goal-oriented architectures (understanding Apps, Agents, Sub-agents, and Sessions within CX Agent Studio).\nPrompt Engineering & Context Management: Writing robust system instructions, managing conversational memory, and optimizing LLM context windows for voice interactions.\nDialogflow CX Fundamentals: Understanding the underlying mechanics of Dialogflow CX,\n Google Cloud Platform(GCP):\nCloud Compute & Serverless: Deploying agent components, webhook integrations, or backend APIs using Google Cloud Functions or Cloud Run.\ngcloud CLI Mastery: Utilizing gcloud for project configuration and authenticating environments via application-default credentials.\n API Integration & Tool Building:\nTool Calling / Function Calling: Designing and registering external APIs (\"Tools\") that the LLM can invoke to retrieve data, execute backend tasks, or interact with external services.\nData Handling & Payload Parsing: Using utility functions to handle pagination, flatten API responses, and convert complex Protocol Buffers (Protos) into usable data.\n Testing, Evaluation & CI/CD:\nAutomated Agent Evaluation (Evals): Creating and orchestrating \"Golden tests\" and automated simulation runs using SCRAPI’s evals module.\nPerformance Metrics Tracking: Extracting, analyzing, and optimizing agent performance metrics (like real-time latency), which is highly critical for voice voice interactions.\nAgentic IDE workflows: Using LLM-assisted development tools (like Gemini CLI or Claude Code) as integrated into the SCRAPI workflow to speed up agent scaffolding and debugging.\n Key Skills:\n Python:\nPython Programming (Advanced):. You must be proficient in Python, utilizing object-oriented programming, modern type hinting, and asynchronous patterns.\nEnvironment & Dependency Management: Familiarity with modern Python package managers like uv, virtual environments (.venv), and pip.\nCommand Line Interface (CLI) Proficiency: Ability to navigate CLI tools, as well as general shell scripting\n Conversational AI / Agent Architecture:\nGenerative Agent Design: Shifting from legacy intent-based state machines to generative, goal-oriented architectures (understanding Apps, Agents, Sub-agents, and Sessions within CX Agent Studio).\nPrompt Engineering & Context Management: Writing robust system instructions, managing conversational memory, and optimizing LLM context windows for voice interactions.\nDialogflow CX Fundamentals: Understanding the underlying mechanics of Dialogflow CX,\n Google Cloud Platform(GCP):\nCloud Compute & Serverless: Deploying agent components, webhook integrations, or backend APIs using Google Cloud Functions or Cloud Run.\ngcloud CLI Mastery: Utilizing gcloud for project configuration and authenticating environments via application-default credentials.\n API Integration & Tool Building:\nTool Calling / Function Calling: Designing and registering external APIs (\"Tools\") that the LLM can invoke to retrieve data, execute backend tasks, or interact with external services.\nData Handling & Payload Parsing: Using utility functions to handle pagination, flatten API responses, and convert complex Protocol Buffers (Protos) into usable data.\n Testing, Evaluation & CI/CD:\nAutomated Agent Evaluation (Evals): Creating and orchestrating \"Golden tests\" and automated simulation runs using SCRAPI’s evals module.\nPerformance Metrics Tracking: Extracting, analyzing, and optimizing agent performance metrics (like real-time latency), which is highly critical for voice voice interactions.\nAgentic IDE workflows: Using LLM-assisted development tools (like Gemini CLI or Claude Code) as integrated into the SCRAPI workflow to speed up agent scaffolding and debugging.\n Key Skills:\n Python:\nPython Programming (Advanced):. 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