AI Architect

Key Skills: Python: Python Programming (Advanced):. You must be proficient in Python, utilizing object-oriented programming, modern type hinting, and asynchronous patterns. Environment & Dependency Management: Familiarity with modern Python package managers like uv, virtual environments (.venv), and pip. Command Line Interface (CLI) Proficiency: Ability to navigate CLI tools, as well as general shell scripting Conversational AI / Agent Architecture: Generative 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). Prompt Engineering & Context Management: Writing robust system instructions, managing conversational memory, and optimizing LLM context windows for voice interactions. Dialogflow CX Fundamentals: Understanding the underlying mechanics of Dialogflow CX, Google Cloud Platform(GCP): Cloud Compute & Serverless: Deploying agent components, webhook integrations, or backend APIs using Google Cloud Functions or Cloud Run. gcloud CLI Mastery: Utilizing gcloud for project configuration and authenticating environments via application-default credentials. API Integration & Tool Building: Tool 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. Data Handling & Payload Parsing: Using utility functions to handle pagination, flatten API responses, and convert complex Protocol Buffers (Protos) into usable data. Testing, Evaluation & CI/CD: Automated Agent Evaluation (Evals): Creating and orchestrating "Golden tests" and automated simulation runs using SCRAPI’s evals module. Performance Metrics Tracking: Extracting, analyzing, and optimizing agent performance metrics (like real-time latency), which is highly critical for voice voice interactions. Agentic 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. Key Skills: Python: Python Programming (Advanced):. You must be proficient in Python, utilizing object-oriented programming, modern type hinting, and asynchronous patterns. Environment & Dependency Management: Familiarity with modern Python package managers like uv, virtual environments (.venv), and pip. Command Line Interface (CLI) Proficiency: Ability to navigate CLI tools, as well as general shell scripting Conversational AI / Agent Architecture: Generative 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). Prompt Engineering & Context Management: Writing robust system instructions, managing conversational memory, and optimizing LLM context windows for voice interactions. Dialogflow CX Fundamentals: Understanding the underlying mechanics of Dialogflow CX, Google Cloud Platform(GCP): Cloud Compute & Serverless: Deploying agent components, webhook integrations, or backend APIs using Google Cloud Functions or Cloud Run. gcloud CLI Mastery: Utilizing gcloud for project configuration and authenticating environments via application-default credentials. API Integration & Tool Building: Tool 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. Data Handling & Payload Parsing: Using utility functions to handle pagination, flatten API responses, and convert complex Protocol Buffers (Protos) into usable data. Testing, Evaluation & CI/CD: Automated Agent Evaluation (Evals): Creating and orchestrating "Golden tests" and automated simulation runs using SCRAPI’s evals module. Performance Metrics Tracking: Extracting, analyzing, and optimizing agent performance metrics (like real-time latency), which is highly critical for voice voice interactions. Agentic 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. Key Skills: Python: Python Programming (Advanced):. You must be proficient in Python, utilizing object-oriented programming, modern type hinting, and asynchronous patterns. Environment & Dependency Management: Familiarity with modern Python package managers like uv, virtual environments (.venv), and pip. Command Line Interface (CLI) Proficiency: Ability to navigate CLI tools, as well as general shell scripting Conversational AI / Agent Architecture: Generative 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). Prompt Engineering & Context Management: Writing robust system instructions, managing conversational memory, and optimizing LLM context windows for voice interactions. Dialogflow CX Fundamentals: Understanding the underlying mechanics of Dialogflow CX, Google Cloud Platform(GCP): Cloud Compute & Serverless: Deploying agent components, webhook integrations, or backend APIs using Google Cloud Functions or Cloud Run. gcloud CLI Mastery: Utilizing gcloud for project configuration and authenticating environments via application-default credentials. API Integration & Tool Building: Tool 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. Data Handling & Payload Parsing: Using utility functions to handle pagination, flatten API responses, and convert complex Protocol Buffers (Protos) into usable data. Testing, Evaluation & CI/CD: Automated Agent Evaluation (Evals): Creating and orchestrating "Golden tests" and automated simulation runs using SCRAPI’s evals module. Performance Metrics Tracking: Extracting, analyzing, and optimizing agent performance metrics (like real-time latency), which is highly critical for voice voice interactions. Agentic 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.