You will get an LLM fine-tuning and deployment solution on Azure/AWS/GCP
Project details
I help businesses fine-tune open-source and commercial LLMs (Llama 3, Mistral, GPT, Falcon) on custom datasets using LoRA, QLoRA, RLHF, and DPO techniques. Models are trained on Azure ML, AWS SageMaker, or GCP Vertex AI, then evaluated and deployed via REST APIs. I cover the full pipeline — data prep, training, evaluation, and deployment. Ideal for domain-specific chatbots, code assistants, classification models, and intelligent search systems. You get clean source code, model weights, and a live API endpoint.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, MLflow, PyTorch, TensorFlowWhat's included
| Service Tiers |
Starter
$800
|
Standard
$1,800
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
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Sunny is exceptionally good at what he does. Even after a project is completed, he remains highly responsive to feedback and is always willing to help resolve any issues or integration hiccups that may arise. His support doesn’t end with project delivery, which is truly commendable.
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Aug 13, 2026
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What a professional! Sunny did a really good job throughout the project. He understood the requirement well and delivered impressive reconstruction quality. I was particularly happy with the live demonstration, where the results were very close to the original images. He was also responsive and clear in his communication throughout the project. Overall, a very smooth and positive experience working with him.
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Professional
Instantaneous
Always available
Quick work feedback and Logical. This is why I would recommend him. I work a organization as well I understand what a professional should work like. Kudos!
Instantaneous
Always available
Quick work feedback and Logical. This is why I would recommend him. I work a organization as well I understand what a professional should work like. Kudos!
About Sunny
AI Agent Engineer | MCP, RAG, LangGraph, Copilot Studio & Azure
100%
Job Success
Chandigarh, India - 9:23 am local time
I have 6 years of experience delivering production-grade AI and data systems in Fortune 500 retail, consulting, financial-data, and cybersecurity environments.
SELECTED RESULTS
• Built a real-time multi-agent cybersecurity platform that improved incident-response speed by 40%.
• Delivered an image-to-data pipeline with 95%+ accuracy across complex document types.
• Built natural-language-to-SQL assistants that eliminated manual query writing and enabled non-technical teams to access live business data.
• Developed enterprise document and workflow automation using private data, APIs, cloud services, and human approval controls.
WHAT I BUILD
• Enterprise AI agents and multi-agent workflows using LangGraph, LangChain, AutoGen, and LlamaIndex
• Production RAG systems over documents, databases, and business knowledge
• Document intelligence, extraction, classification, and structured-output pipelines
• Natural-language SQL and analytics assistants
• MCP, API, ERP, and business-workflow integrations
• Cloud deployment and production hardening on Azure and AWS
DELIVERY
I can own the complete lifecycle: requirements discovery, architecture, prototype, retrieval and data layer, API backend, evaluation, deployment, monitoring, and support.
CORE STACK
Python, FastAPI, LangGraph, LangChain, LlamaIndex, AutoGen, Azure OpenAI, AWS Bedrock, OpenAI, Claude, Gemini, Pinecone, Weaviate, PostgreSQL, Docker, Kubernetes, Databricks, and Kafka.
WHY CLIENTS HIRE ME
• Production systems, not isolated prompt demos
• Full-stack AI ownership from architecture through deployment
• Clear communication, documented decisions, and realistic estimates
• Enterprise integration experience with private data and controlled workflows
Building an AI agent, RAG system, document-intelligence workflow, or enterprise LLM application? Send me the problem, current systems, and desired outcome. I will propose the smallest reliable path to production.
Steps for completing your project
After purchasing the project, send requirements so Sunny can start the project.
Delivery time starts when Sunny receives requirements from you.
Sunny works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preparation & Model Selection
We review your dataset, clean and format it (JSONL/CSV), select the right base model (Llama 3, Mistral, GPT, etc.), and set up your cloud training environment on Azure, AWS, or GCP.
Fine-Tuning & Evaluation
We run fine-tuning using LoRA/QLoRA (or full fine-tuning for smaller models), track experiments, evaluate outputs with BLEU/ROUGE/custom metrics, and iterate until quality benchmarks are met.