Advanced Agentic AI Chatbot Developer
Budget: ₹1,500 – ₹12,500 INR
Job Title:
Build an Advanced Agentic Chatbot with Voice Interaction and Dynamic Visual Port
Job Description:
We are looking for an experienced developer (or team) to create an agentic chatbot solution that features voice-based conversation, AI-driven explanations, and dynamic visual content in a visual port. Our goal is to build a robust, scalable platform that integrates an advanced AI model (e.g., Google GenAI or another leading large language model) for natural language understanding and generation, along with a Python-powered service for producing dynamic animations or images.
Key Requirements & Technologies
Agentic Framework & Model Context Protocol
Strong familiarity with agentic frameworks for AI-driven conversation management.
Ability to maintain conversational state and context across multiple user queries.
Experience with GraphRAG or similar retrieval-augmented generation techniques is a plus.
AI Backend (Node.js + Express.js)
Development of a “Unified AI Agent” that interfaces with the chosen advanced AI model for audio input/output and text-based conversation.
Proficiency with Node.js (Express.js) to set up RESTful APIs and WebSockets.
Experience with conversation context handling (potentially via libraries such as LangChain or Semantic Kernel, though not mandatory).
Visual Port Rendering Service (Python)
A dedicated Python-based component that receives rendering instructions and outputs dynamic visuals (videos or images) to the visual port.
Familiarity with task queues (e.g., Celery or Redis) for asynchronous rendering tasks.
Efficient file handling and caching mechanisms to reduce redundant rendering.
WebSocket-based updates to notify the front end of completed render jobs.
Frontend (Next.js + React + TypeScript)
Expertise in Next.js (React) with TypeScript for building a modern, responsive UI.
Implementation of real-time communication using WebSockets.
Handling of audio recording and playback via the Web Audio API.
Display of rendered video (mp4) or image (png) outputs with standard HTML5 tags.
Integration of user-facing features (e.g., a “visual port” area for dynamic visuals, text output area, chatbot avatar, etc.).
Styling with Tailwind CSS (or a similar framework).
Voice Interaction
End-to-end audio pipeline: capturing the user’s voice in the browser, sending it to the AI backend, receiving AI-generated audio replies.
Seamless control of audio input/output states in the frontend.
System Architecture & Communication Flow
Clear design for how the frontend communicates with the AI backend, which then interacts with the advanced AI model and, if needed, delegates a rendering request to the Python service.
Real-time or near-real-time updates of the conversation and visual elements.
Use of a Model Context Protocol to ensure conversation continuity and context management.
Testing & Deployment
Strong emphasis on unit and integration tests for all major components.
Experience with scalable deployment solutions (e.g., Docker, Kubernetes, AWS, GCP).
Performance & Scalability
Awareness of performance bottlenecks (especially during real-time data transfer and rendering).
Ability to optimize resource usage for rendering and networking.
Security & Error Handling
Ensuring secure data transfer (SSL/TLS).
Robust error logging, graceful fallback mechanisms, and strong input validation.
Deliverables
Fully functioning chatbot UI built in Next.js + TypeScript with voice recording/playback, text display, and real-time visual updates.
Express.js (Node.js) backend that coordinates audio input with the chosen advanced AI model, handles conversation flow, and orchestrates requests to the Python service.
Python-based rendering service for dynamic visuals, plus a task queue (Celery/Redis) for asynchronous processing and WebSocket-based status updates.
Complete integration demonstrating voice query, AI-generated response, and dynamic visual output returned to the front end.
Documentation for setup, deployment, and maintenance.
Test coverage (unit/integration) to ensure reliability.
How to Apply
Portfolio/Experience: Please share examples of agentic AI projects, especially those involving real-time audio processing, advanced UI, or Python-based dynamic rendering.
Technical Approach: Briefly outline how you would implement the AI <-> rendering service <-> frontend interaction flow, your familiarity with GraphRAG or similar frameworks, and any relevant DevOps/deployment expertise.
Availability & Timeline: Indicate your expected project schedule and milestones.
Budget: Provide your estimated cost or hourly rate.
If you have the expertise in building agentic chatbots with advanced AI integration, dynamic Python-based rendering, and robust Next.js front-end skills, we want to hear from you!
Build an Advanced Agentic Chatbot with Voice Interaction and Dynamic Visual Port
Job Description:
We are looking for an experienced developer (or team) to create an agentic chatbot solution that features voice-based conversation, AI-driven explanations, and dynamic visual content in a visual port. Our goal is to build a robust, scalable platform that integrates an advanced AI model (e.g., Google GenAI or another leading large language model) for natural language understanding and generation, along with a Python-powered service for producing dynamic animations or images.
Key Requirements & Technologies
Agentic Framework & Model Context Protocol
Strong familiarity with agentic frameworks for AI-driven conversation management.
Ability to maintain conversational state and context across multiple user queries.
Experience with GraphRAG or similar retrieval-augmented generation techniques is a plus.
AI Backend (Node.js + Express.js)
Development of a “Unified AI Agent” that interfaces with the chosen advanced AI model for audio input/output and text-based conversation.
Proficiency with Node.js (Express.js) to set up RESTful APIs and WebSockets.
Experience with conversation context handling (potentially via libraries such as LangChain or Semantic Kernel, though not mandatory).
Visual Port Rendering Service (Python)
A dedicated Python-based component that receives rendering instructions and outputs dynamic visuals (videos or images) to the visual port.
Familiarity with task queues (e.g., Celery or Redis) for asynchronous rendering tasks.
Efficient file handling and caching mechanisms to reduce redundant rendering.
WebSocket-based updates to notify the front end of completed render jobs.
Frontend (Next.js + React + TypeScript)
Expertise in Next.js (React) with TypeScript for building a modern, responsive UI.
Implementation of real-time communication using WebSockets.
Handling of audio recording and playback via the Web Audio API.
Display of rendered video (mp4) or image (png) outputs with standard HTML5 tags.
Integration of user-facing features (e.g., a “visual port” area for dynamic visuals, text output area, chatbot avatar, etc.).
Styling with Tailwind CSS (or a similar framework).
Voice Interaction
End-to-end audio pipeline: capturing the user’s voice in the browser, sending it to the AI backend, receiving AI-generated audio replies.
Seamless control of audio input/output states in the frontend.
System Architecture & Communication Flow
Clear design for how the frontend communicates with the AI backend, which then interacts with the advanced AI model and, if needed, delegates a rendering request to the Python service.
Real-time or near-real-time updates of the conversation and visual elements.
Use of a Model Context Protocol to ensure conversation continuity and context management.
Testing & Deployment
Strong emphasis on unit and integration tests for all major components.
Experience with scalable deployment solutions (e.g., Docker, Kubernetes, AWS, GCP).
Performance & Scalability
Awareness of performance bottlenecks (especially during real-time data transfer and rendering).
Ability to optimize resource usage for rendering and networking.
Security & Error Handling
Ensuring secure data transfer (SSL/TLS).
Robust error logging, graceful fallback mechanisms, and strong input validation.
Deliverables
Fully functioning chatbot UI built in Next.js + TypeScript with voice recording/playback, text display, and real-time visual updates.
Express.js (Node.js) backend that coordinates audio input with the chosen advanced AI model, handles conversation flow, and orchestrates requests to the Python service.
Python-based rendering service for dynamic visuals, plus a task queue (Celery/Redis) for asynchronous processing and WebSocket-based status updates.
Complete integration demonstrating voice query, AI-generated response, and dynamic visual output returned to the front end.
Documentation for setup, deployment, and maintenance.
Test coverage (unit/integration) to ensure reliability.
How to Apply
Portfolio/Experience: Please share examples of agentic AI projects, especially those involving real-time audio processing, advanced UI, or Python-based dynamic rendering.
Technical Approach: Briefly outline how you would implement the AI <-> rendering service <-> frontend interaction flow, your familiarity with GraphRAG or similar frameworks, and any relevant DevOps/deployment expertise.
Availability & Timeline: Indicate your expected project schedule and milestones.
Budget: Provide your estimated cost or hourly rate.
If you have the expertise in building agentic chatbots with advanced AI integration, dynamic Python-based rendering, and robust Next.js front-end skills, we want to hear from you!