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AI Architect

exl Service.com (India) Private Limited
Noida, IND
Full-time
Mid
Hybrid
Discovered 4 days ago
gcpgeminipython

Full Job Posting

Qualifications

  • 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.

Responsibilities

  • 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.

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