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EXL

Data Scientist

EXL Noida, Uttar Pradesh, India

Business Consulting and Services · 10,001+ employees

Oct 07
data-scientist Senior (5-10 yrs) Full-time India
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About the role

Design and deploy generative, goal-oriented conversational agents using Dialogflow CX and Google Cloud Platform. Develop backend integrations, manage API tool calling, and implement automated testing and performance monitoring for voice interactions.

What they look for

Python Object-oriented programming Asynchronous programming Dialogflow CX Generative AI Prompt engineering Google Cloud Platform Cloud Functions Cloud Run Gcloud CLI API integration Protocol Buffers Automated testing Performance metrics Shell scripting

Requirements

Requires a Bachelor's or Master's degree in Engineering with 3-8 years of experience. Candidates must be proficient in advanced Python programming, CLI tools, and modern cloud-based agent architectures.

Full 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.

Responsibilities

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.

Qualifications

Bachelor's/Master's in Engineering 5-8 years

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