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Fiverr Pro
English
$
USD
I will build, fine tune, and deploy custom machine learning and llm models
About this Gig
Looking for an expert Machine Learning Engineer to build custom ML models, fine-tune LLMs, or deploy production AI pipelines? You are in the right place!
I specialize in crafting end-to-end Machine Learning and Generative AI solutions tailored to your specific business or research needs. From data cleaning to parameter-efficient fine-tuning (QLoRA/LoRA) and API deployment, I deliver clean, production-ready code.
What I Offer:
- Custom Machine Learning: Classification, Regression, XGBoost, Scikit-Learn pipelines, and feature engineering.
- LLM Fine-Tuning & GenAI: Parameter-efficient fine-tuning (QLoRA/LoRA on Llama 3/Hugging Face models), RAG applications, and Gemini/OpenAI API integration.
- MLOps & Deployment: REST APIs using Flask/FastAPI, Docker containerization, FAISS vector database integration, and cloud deployment.
- Model Evaluation & Optimization: Hyperparameter tuning, experiment tracking with MLflow, and detailed technical documentation.
Why Choose Me?
- Hands-on research experience in LLM optimization (reducing memory usage by ~40% using 4-bit quantization).
- Experience building real-world AI applications, including real-time video surveillance and automated data extraction
Programming language:
Python
•
SQL
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
•
Other
Tools:
Jupyter Notebook
•
OpenCV
•
Excel
•
CVAT
•
Colab
Other Data Science & ML Services I Offer
FAQ
What information or files do I need to provide before placing an order?
Please provide a clear description of your project goals, your dataset (if available), and any specific performance metrics or compute constraints (e.g., GPU memory, cloud environment). If you don't have a dataset yet, I can help advise on data collection or preprocessing.
Can you fine-tune Large Language Models (LLMs) on custom datasets?
Yes! I specialize in parameter-efficient fine-tuning (PEFT) using QLoRA and LoRA techniques on models like Llama 3, Mistral, and Hugging Face architectures. I can optimize models to run efficiently on low-resource GPUs (e.g., single T4 GPU) while maintaining high accuracy.
What machine learning and deep learning frameworks do you use?
I primarily work with Python, PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers, OpenCV, and FAISS for vector search. For web API deployment, I use Flask and FastAPI.
Will I receive the complete source code and model files?
Yes! Every package includes clean, fully documented Python source code (Jupyter Notebooks or modular scripts) along with trained model weights and step-by-step setup instructions.
Can you integrate the ML model into a working web application or API?
Absolutely. In the Premium package (or via Gig Extras), I can build a Flask or FastAPI backend, create interactive dashboards using Streamlit, or set up Docker containers for seamless deployment.
What if my dataset is messy, unformatted, or incomplete?
No problem! Data preprocessing, missing value handling, feature engineering, and data cleaning are core parts of my ML workflow across all packages.
How do we ensure model quality and performance?
I evaluate models using standard metrics (Accuracy, Precision, Recall, F1-Score, ROUGE scores for NLP, or R² for regression) and track experiments to ensure reproducible, optimal results.
