1:00 PM - 2:00 PM
Senior Product Manager Interview
Sarah Jenkins
Location
onsite, Noida, Uttar Pradesh, India
Commitment
Full Time
Level
Junior (<2 years)
Required skills
PythonGenerative AIDialogflow CXGoogle Cloud PlatformCloud RunGoogle Cloud FunctionsPrompt EngineeringAPI IntegrationFunction CallingCI/CDAutomated TestingShell ScriptingAsynchronous ProgrammingProtocol BuffersLLM Context Management
Job Description
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 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.
