Data Scientist

EXL

. Develop GECX conversational AI agents using Dialogflow CX .

Posted 10/7/2026full-timeNoida • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing conversational AI agents using Dialogflow CX, with a strong focus on generative architectures, performance optimization, and integration with Google Cloud services. Proficient in Python programming and familiar with advanced development tools and methodologies for enhancing agent functionality.

Highest-signal resume keywords

Dialogflow CX DevelopmentPython ProgrammingGoogle Cloud FunctionsConversational AI ArchitectureAgent Performance Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonObject-Oriented ProgrammingAsynchronous PatternsPrompt EngineeringAPI DesignProtocol BuffersAutomated Agent EvaluationConversational Memory ManagementLLM Context OptimizationShell Scripting

Tools & Technologies

Dialogflow CXGoogle Cloud RunGcloud CLISCRAPI’s Evals ModuleGemini CLIClaude Code

Certifications & Qualifications

Bachelor's Degree in EngineeringMaster's Degree in Engineering

Industry Keywords

Conversational AIGenerative Agent ArchitecturesReal-Time LatencyAgent Performance MetricsVoice Interactions

Tech Stack

Tools & technologies

CloudPythonShell Scripting

About the role

Key responsibilities & impact

Develop GECX conversational AI agents using Dialogflow CX
Build generative, goal-oriented agent architectures
Write system instructions and manage conversational memory and LLM context for voice interactions
Deploy agent components, webhook integrations, and backend APIs using Google Cloud Functions or Cloud Run
Design and register external APIs/tools for LLM tool and function calling
Handle pagination, flatten API responses, and convert Protocol Buffers into usable data
Create and orchestrate automated agent evaluations, Golden tests, and simulation runs using SCRAPI’s evals module
Extract, analyze, and optimize agent performance metrics such as real-time latency
Use LLM-assisted IDE workflows to accelerate agent scaffolding and debugging

Requirements

What you’ll need

Bachelor's/Master's in Engineering
5–8 years of experience
Advanced proficiency in Python, including object-oriented programming, modern type hinting, and asynchronous patterns
Familiarity with uv, virtual environments (.venv), and pip
CLI tools and general shell scripting
Conversational AI and Dialogflow CX fundamentals
Generative, goal-oriented agent architectures, including Apps, Agents, Sub-agents, and Sessions within CX Agent Studio
Prompt engineering, conversational memory management, and LLM context-window optimization for voice interactions
Google Cloud Functions or Cloud Run
gcloud CLI and application-default credentials
Designing and registering external APIs/tools for LLM tool or function calling
Pagination, API response flattening, and Protocol Buffers payload parsing
Automated agent evaluation, Golden tests, and SCRAPI’s evals module
Agent performance metrics analysis and optimization, including real-time latency
LLM-assisted development tools such as Gemini CLI or Claude Code