Google CX Developer

Data Scientists design and implement data analysis processes to derive actionable insights. Duties include building statistical models, analyzing large datasets, and presenting findings to stakeholders. You will collaborate with other data professionals to solve complex problems and improve business outcomes. Proficiency in data science tools and techniques is essential.

List of Skills

Python

• Python Programming (Advanced): SCRAPI is a Python-based API. 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.

Bachelor's/Master's in Engineering 2-5 years