Data Scientist

Fa Ewjt Saasfaprod1; Dadri; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Fa Ewjt Saasfaprod1 is seeking a GECX Developer with strong Python skills and experience in Dialogflow CX, GCP, and tool integration. You will design generative agent architectures, craft robust prompts, and manage context for sophisticated conversational systems.

The role involves building and deploying agent components on Google Cloud, leveraging gcloud for configuration, and implementing tool calls and data parsing.

Qualifications

• Proficient in Python with object-oriented programming, modern type hints, and asynchronous patterns.
• Experience with Dialogflow CX and Google Cloud Platform (GCP).
• Familiar with CI/CD, automated evaluations, and performance metrics.
• Strong CLI skills and scripting abilities.

Skills

Python

CLI Proficiency

Generative Agent Design

Prompt Engineering

Dialogflow CX Fundamentals

GCP

gcloud CLI

Tool Calling

Payload Parsing

Automated Agent Evaluation

Performance Metrics Tracking

Agentic IDE workflows

Bachelor's/Master's in Engineering

Education

Bachelor's/Master's in Engineering

Job description

Job description: GECX Developer

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 (Dialogflow CX)

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.

Job description: GECX Developer

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 (Dialogflow CX)

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.

Job description: GECX Developer

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 (Dialogflow CX)

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 5-8 years