You will get Custom Recommendation System | Machine learning developer | ML/AI | Python
Top Rated
Project details
AI-Powered Recommendation System:
This project involved the end-to-end development of a Machine Learning-driven Recommendation System designed to deliver hyper-personalized experiences tailored to specific business objectives. With 7+ years of experience in AI/ML, I led the design, training, and deployment of advanced recommender engines that improved customer engagement, operational efficiency, and measurable ROI.
The system architecture incorporated a range of modern approaches—including Large Language Models (LLMs), neural networks, and reinforcement learning algorithms—to adapt to complex user behavior patterns. My responsibilities spanned from data preprocessing and exploratory analysis to model training, fine-tuning, and production-grade deployment.
The solution has been successfully implemented across fintech, retail, and iGaming industries, where it significantly optimized the user journey, enhanced conversions, and improved long-term customer retention. Using generative AI, transformer-based models, and industry-best frameworks, I delivered a scalable and high-performing system designed to evolve with growing data and user needs.
This project involved the end-to-end development of a Machine Learning-driven Recommendation System designed to deliver hyper-personalized experiences tailored to specific business objectives. With 7+ years of experience in AI/ML, I led the design, training, and deployment of advanced recommender engines that improved customer engagement, operational efficiency, and measurable ROI.
The system architecture incorporated a range of modern approaches—including Large Language Models (LLMs), neural networks, and reinforcement learning algorithms—to adapt to complex user behavior patterns. My responsibilities spanned from data preprocessing and exploratory analysis to model training, fine-tuning, and production-grade deployment.
The solution has been successfully implemented across fintech, retail, and iGaming industries, where it significantly optimized the user journey, enhanced conversions, and improved long-term customer retention. Using generative AI, transformer-based models, and industry-best frameworks, I delivered a scalable and high-performing system designed to evolve with growing data and user needs.
Machine Learning Tools
Amazon SageMaker, ChatGPT, MLflow, NLTK, Open Neural Network Exchange, OpenCV, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, TensorFlow, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$6,000
|
Advanced
$30,000
|
|---|---|---|---|
| Delivery Time | 1 day | 40 days | 70 days |
Number of Revisions | 0 | 2 | 3 |
Number of Model Variations | 0 | 2 | 3 |
Number of Scenarios | 0 | 0 | 2 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - |
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AA
Adrian A.
Mar 24, 2026
AI Agent Development
Working with Asif Chaudhary was an excellent experience! He is highly skilled in development and bug fixing, delivering top-quality work with great attention to detail. Communication was clear and timely throughout the project. I would gladly work with him again and highly recommend his services.
BJ
Bruce J.
Mar 10, 2026
Python ML Developer for NLP Clustering Prototype
OK
SI
Safdar I.
Aug 10, 2025
Fix & Deploy Advanced AI Dialogue System with n8n implementation
Asif delivered good work on the Deployment Dialogue system project His communication was top-notch, he met all deadlines despite it was very tight one, and his skills were reasonably strong. Highly Recommended.
About Asif
Generative AI Engineer | LLM, RAG, AI Agents | AI Automation | Python
100%
Job Success
San Francisco, United States - 7:56 pm local time
Most AI automation projects fail when they need to work in production, not in a demo. I design and build production-ready generative AI systems using OpenAI (GPT-4/5), Claude, Gemini, and open-source LLMs like Llama and Mistral, with clean Python backends that integrate into the tools your team already uses.
What I build:
Generative AI and AI automation systems for business workflows
AI agents for customer support, operations, and internal tools (LangChain, LangGraph, CrewAI)
RAG (Retrieval-Augmented Generation) knowledge systems connected to docs, PDFs, and databases
AI chatbots and assistants that take real actions, not just answer questions
Voice AI agents for inbound and outbound calls: appointment booking, lead qualification, and support (Twilio, VAPI, Retell)
Claude automation and Claude Code agent workflows
Custom GPT systems, prompt engineering, and structured outputs (JSON schema control)
Workflow automation with n8n, Make, Zapier, and custom Python scripts
Backend systems for LLM applications: APIs, integrations, and AI SaaS features
Fine-tuning and deployment of open-source models (LoRA, QLoRA, Hugging Face)
Results so far:
100% Job Success Score on Upwork
30+ AI and automation projects delivered (Upwork and direct clients)
10,000+ hours of manual work automated across client teams
Typical response time: under 1 hour
When clients reach out:
Repetitive manual processes are slowing the team down
Internal knowledge is scattered across docs, PDFs, and systems
Support or operations need AI automation
Phone-based support, booking, or lead intake needs to run without a human on every call
They want real generative AI functionality inside an existing product or SaaS
A previous AI solution worked as a demo but failed in real-world usage
How I approach projects:
I build AI systems that work in practice, not just demos. That means designing around your actual workflows, keeping the backend clean and maintainable, and making sure the LLM, RAG pipeline, or agent integrates properly with your data and tools. Clear communication, iterative delivery, and production-ready code.
Tech stack:
LLMs: OpenAI GPT-4/5, Claude, Gemini, Llama, Mistral
Frameworks: LangChain, LangGraph, CrewAI, LlamaIndex, Hugging Face
RAG and vector databases: Pinecone, FAISS, Chroma, pgvector
Backend: Python, FastAPI, Flask, Django
Automation: n8n, Make, Zapier, API and webhook orchestration
Voice AI: Twilio, VAPI, Retell, speech-to-text and text-to-speech pipelines
Infrastructure: Docker, AWS, GCP, REST APIs
If you are building something with generative AI, or trying to figure out the right approach, message me with your use case. I typically respond within an hour and can help you scope it into something that actually works in production.
Keywords: Generative AI Engineer, AI Engineer, LLM Engineer, AI Automation, AI Agents, RAG, Retrieval Augmented Generation, Chatbot Development, Voice AI Agent, Voice Agent Development, AI Phone Agent, Conversational AI, OpenAI API, GPT-4, Claude API, Claude Automation, Gemini, Llama, LangChain, LangGraph, CrewAI, LlamaIndex, Prompt Engineering, Fine-Tuning, Python, FastAPI, Backend Development, API Integration, Workflow Automation, n8n, Zapier, Twilio, VAPI, Speech to Text, NLP, Semantic Search, Vector Database, Pinecone, FAISS, ChromaDB, Knowledge Base Chatbot, PDF Chatbot, Embeddings, AI SaaS Development
Steps for completing your project
After purchasing the project, send requirements so Asif can start the project.
Delivery time starts when Asif receives requirements from you.
Asif works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Consultation
Discuss business needs, goals, and existing infrastructure to define the project's scope and expectations. Gather all necessary details and datasets for analysis.
Data Analysis & Preparation
Assess dataset quality, clean data, and prepare features to align with business objectives. Ensure data is ready for building a recommendation model.