Data Scientist
EXL · Noida, Uttar Pradesh, India ·
- Work mode
- Hybrid
- Employment
- Full time
- Category
- Data science
- Experience
- 5+ years
EXL · Noida, Uttar Pradesh, India ·
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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
mexdigital · Dubai Office