You will get Fine tuned LLM on your business data for better results
Project details
Generic LLMs give generic answers. If you need a model that actually understands your domain — your terminology, your edge cases, your data — fine-tuning is the way to go. I'll train a custom model on your data so it responds like a domain expert, not a generalist.
I use parameter-efficient methods (LoRA, QLoRA) so you don't need a massive GPU budget. The process: I clean and format your data, select the right base model, run training with experiment tracking, evaluate against your success criteria, and deliver a deployment-ready model with inference code.
Every experiment gets logged in MLflow so you can see exactly what was tried and why the final model was chosen. Your data stays private throughout. I fine-tune models daily in my current work — this isn't theoretical for me, it's hands-on.
I use parameter-efficient methods (LoRA, QLoRA) so you don't need a massive GPU budget. The process: I clean and format your data, select the right base model, run training with experiment tracking, evaluate against your success criteria, and deliver a deployment-ready model with inference code.
Every experiment gets logged in MLflow so you can see exactly what was tried and why the final model was chosen. Your data stays private throughout. I fine-tune models daily in my current work — this isn't theoretical for me, it's hands-on.
AI Algorithms
Feedforward Neural Network, Large Language Model, Transformer ModelAI Applications
AI Chatbot, Machine Translation, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Object Detection, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, Microsoft 365 Copilot, PyTorch, TensorFlow, Word2vecAI Models
ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 10 days | 18 days | 28 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | ||
Database Integration | - | - | |
Detailed Code Comments | |||
Image Upscaling | - | - | |
MLOps | - | ||
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | |||
Model Tuning | |||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | - | ||
Source Code |
Frequently asked questions
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MH
Muhammad Uzair H.
Apr 17, 2024
Need a Full-Stack Developer for Event Management System (Java Spring Boot + ReactJS)
Working with Ali was a fantastic experience! His professionalism, attention to detail, and exceptional skill in turning our vision into reality were outstanding. He delivered the project with great quality and speed. Highly recommended for anyone looking for top-tier development work.
About Muhammad
AI/ML Engineer & Java Backend Dev | LLMs - RAG - PyTorch - Spring Boot
Barcelona, Spain - 9:29 pm local time
Over the past 4+ years, I've shipped Java Spring Boot microservices for enterprise clients like Intel and Roche, trained and fine-tuned deep learning models for real-world use cases at companies like ASML, and built LLM-powered tools that people actually rely on. I also have an MSc in Artificial Intelligence (Erasmus Mundus) across three European universities, focused on multimodal AI and model evaluation.
What I can build for you:
AI/ML & LLM Engineering:
→ RAG pipelines and retrieval systems (BM25, cross-encoder reranking, FAISS, vector databases)
→ LLM application development (LangChain, Hugging Face, OpenAI/Claude API integration)
→ Fine-tuning LLMs and VLMs using LoRA, QLoRA, PEFT, and PyTorch
→ Deep learning model training — CNNs, transformers, sequence models, object detection (YOLO)
→ ML experiment tracking, evaluation pipelines, and model deployment (MLflow, Docker)
→ AI chatbots, agentic workflows, prompt engineering, and workflow automation
→ Computer vision — detection, classification, multimodal AI, and image pipelines
Backend & Full-Stack Development:
→ Java Spring Boot microservices, REST APIs, and enterprise integrations
→ React / Next.js frontends with modern UI (Tailwind, MUI, responsive design)
→ Database design and optimization (PostgreSQL, Oracle, MySQL, Redis)
→ Docker, CI/CD pipelines, cloud deployment (AWS, Oracle Cloud)
Why clients work with me:
→ 10+ production microservices shipped for Fortune 500 enterprise clients
→ Currently at ASML building multimodal AI systems
→ MSc in Artificial Intelligence — deep technical foundation, not just API wrappers
→ Hands-on with PyTorch, Hugging Face, and model training every day
→ 5.0 rating on Upwork — I deliver clean, well-documented code on time
→ Clear communication, daily updates, and no disappearing acts
Let’s discuss your project — I respond within 2 hours.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Preparation
I review your training data, clean and format it, and create train/validation splits.
Base Model Selection
I recommend and benchmark 2-3 base models on a small sample to find the best starting point.