You will get your custom LLM deployed on RunPod using Docker and vLLM
Project details
With extensive experience in full-stack development and AI deployment, I specialize in building and deploying custom language models (LLMs) that are optimized for performance and scalability. In this project, I will dockerize your custom LLM and deploy it on RunPod using vLLM, ensuring that it runs efficiently in a containerized environment. This approach not only ensures portability but also enhances performance by taking full advantage of hardware acceleration and parallelism.
Your custom LLM will be built to handle large-scale workloads and high concurrency with minimal overhead. By utilizing Docker, the deployment process becomes more streamlined, reproducible, and scalable, making it easier to manage in both development and production environments.
You can expect:
• A fully containerized solution that will be easy to deploy and manage on RunPod.
• Optimized performance with vLLM to accelerate inference speeds.
• A robust, secure deployment ready for production use.
My approach emphasizes strong communication throughout the project to ensure your needs are met, and the deployment process is smooth.
Your custom LLM will be built to handle large-scale workloads and high concurrency with minimal overhead. By utilizing Docker, the deployment process becomes more streamlined, reproducible, and scalable, making it easier to manage in both development and production environments.
You can expect:
• A fully containerized solution that will be easy to deploy and manage on RunPod.
• Optimized performance with vLLM to accelerate inference speeds.
• A robust, secure deployment ready for production use.
My approach emphasizes strong communication throughout the project to ensure your needs are met, and the deployment process is smooth.
AI Development Type
Model Tuning, Recommendation SystemAI Tools
Amazon SageMaker, deeplearn.js, Keras, MLflow, NVIDIA AI Platform, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$400
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 5 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Add Gradio/Streamlit UI for quick demo
(+ 1 Day)
+$75
Integrate LangChain for chatbot/agent use
(+ 3 Days)
+$200
Embedding model deployment (e.g., BGE/Instruct)
(+ 1 Day)
+$80Frequently asked questions
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PA
Pushkar A.
Dec 5, 2025
Python Developer Needed to Set Up AWS Hosting & Manage LiveKit Concurrency
TL
Tom L.
Sep 6, 2025
Convert Excel Macros and Pivot Tables to Web Programs
RC
Romeo C.
Sep 4, 2025
Runpod Severless Create New Storage Volume
Great Job Ajay
ST
Seth T.
May 30, 2025
React Developer Needed for Livekit Chatbox Application
Great working with this developer. Will work with again.
RC
Romeo C.
May 5, 2025
Runpod Compiling
About Ajay
Fullstack Engineer | Python | Generative AI | RAG | Livekit | n8n
100%
Job Success
Mohali, India - 4:50 pm local time
Beyond traditional web development, I also excel in AI-powered chatbot creation, leveraging advanced frameworks like LangChain, RAG models, services like Twilio and deploying LLM models on platforms such as Runpod, AWS, Azure and more.
What I Bring to the Table:
🥇Frontend Development:
✅ Expertise in JavaScript frameworks/libraries: React, Vue, Angular, Svelte, Streamlit
✅ Building modern, responsive interfaces with TailwindCSS, Bootstrap, Material UI
✅ TypeScript for scalable, maintainable frontend architecture
🥇Backend Development:
✅ Proficient in Node.js, Express.js, and Python frameworks like Django, Flask, FastAPI
✅ Headless CMS integration with Strapi for dynamic content management
✅ Development of secure RESTful and GraphQL APIs
✅ Services like Twilio, Stripe, SendGrid
🥇AI Chatbots & AI Calling Solutions:
✅ Designing conversational agents using LangChain and RAG models
✅ Deployment of LLM models for AI-driven applications
✅ Building voice-enabled bots and AI calling solutions using LiveKit, Twilio, Deepgram VAPI
✅ Chatbot integration with OpenAI API, Dialogflow, and custom AI frameworks
✅ Creation and deployment of autonomous AI agents for complex workflows and customer interactions
🥇Database Management:
✅ Skilled with relational and non-relational databases: PostgreSQL, MySQL, MongoDB
✅ Performance optimization for high-traffic applications
🥇Cloud Deployment & DevOps:
✅ Cloud platforms: AWS, GCP, Heroku, Runpod
✅ CI/CD pipelines for seamless deployment and version control
✅ Dockerized application environments for scalability and flexibility
🥇Workflow Automation & Integrations:
✅ Expertise in workflow automation using n8n and Zapier
✅ API integrations across third-party services (CRM, ERP, payment gateways, etc.)
✅ Automating business processes, notifications, and data pipelines
🥇Why Choose Me?
- 🌟 7+ Years of Experience delivering top-notch solutions
- 🎯 Results-Driven Approach: Every project is tailored to meet specific business needs
- 🤝 Reliable Communication: Transparent updates and client-first collaboration
- 💡 Innovation-Focused: Staying at the forefront of tech trends to offer modern solutions
If you're looking for a skilled full-stack developer who can handle everything from frontend interfaces to backend infrastructure—and even cutting-edge AI chatbot development—let’s connect!
Click "Hire Now" to start our journey toward creating exceptional solutions for your business. 🚀
Steps for completing your project
After purchasing the project, send requirements so Ajay can start the project.
Delivery time starts when Ajay receives requirements from you.
Ajay works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Setup and Assessment
Review the provided materials, such as the GitHub repository and documentation, to understand the scope and current status of the project.
Dockerization and Environment Setup
Create a Dockerfile to containerize the application, ensuring it can be run in isolated environments. Set up necessary Docker Compose configurations if required.