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.