You will get Custom Recommender System | Personalized Recommendations with AI
Project details
In today’s competitive market, personalized recommendations drive engagement, retention, and revenue. I create robust, AI-powered recommender systems tailored to your users’ behavior and preferences. From simple collaborative filtering to advanced sequence-based models, I ensure your system delivers actionable insights and personalized experiences.
Using state-of-the-art techniques like hybrid models, embeddings, and deep learning, I integrate your data seamlessly with vector databases like FAISS, Pinecone, or Weaviate for real-time personalization. My expertise with PyTorch, TensorFlow, and Scikit-Learn guarantees scalable and production-ready solutions.
Clients benefit from optimized models, detailed documentation, and clean code with end-to-end pipeline setup. Whether you’re a startup or a global enterprise, my recommender systems increase user satisfaction, drive conversions, and provide measurable ROI. I treat every project as a partnership, ensuring your business goals are fully realized.
Using state-of-the-art techniques like hybrid models, embeddings, and deep learning, I integrate your data seamlessly with vector databases like FAISS, Pinecone, or Weaviate for real-time personalization. My expertise with PyTorch, TensorFlow, and Scikit-Learn guarantees scalable and production-ready solutions.
Clients benefit from optimized models, detailed documentation, and clean code with end-to-end pipeline setup. Whether you’re a startup or a global enterprise, my recommender systems increase user satisfaction, drive conversions, and provide measurable ROI. I treat every project as a partnership, ensuring your business goals are fully realized.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, BERT, ChatGPT, Deeplearning4j, GitHub Copilot, Google AutoML, Google Sheets, GPT-3, Keras, MATLAB, Microsoft Excel, NumPy, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, Scrapy, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$100
|
Standard
$500
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 1 | 2 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $250
Additional Revision
+$30
Additional Model Variation
(+ 2 Days)
+$100
Additional Scenario
(+ 1 Day)
+$40
Additional Graph/Chart
(+ 1 Day)
+$20
Model Documentation
(+ 2 Days)
+$100
Data Source Connectivity
(+ 3 Days)
+$150
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JS
Joanne Da S.
Feb 27, 2024
Looking for C, MicroPython developer
AY
Alex Y.
Sep 18, 2023
Inverter
JS
Joanne Da S.
May 22, 2023
Looking for C, MicroPython developer
Great developer. Actually going to hire again right away
MP
Morgan P.
May 18, 2023
MicroPython Porting Engineer
Had an outstanding experience with this freelancer. He was fast, prompt, and effective, completely solving my problem immediately. Very very grateful to Zain.
Kd
Kasun d.
Nov 29, 2022
Conceptual System Level Paper Design of a Car-Mounted Solar Cell and Wind Generator charger
2nd time with him. Did a very good Job
About Zain
Senior AI Engineer | RAG | LangChain | AI Agents | Agentic AI | LLMOPs
100%
Job Success
Ubauro, Pakistan - 6:09 am local time
⭐️ 56+ completed projects spanning full-stack, machine learning, and generative AI.
⭐️ 16 end-to-end projects: RAG, multimodal RAG, Agentic AI systems, vision-language fine-tuning
Most AI projects stall in the gap between a working notebook and something you can actually ship and maintain — no clean deployment path, no way to evaluate or monitor it.
Closing that gap is my focus.
I'm Zain — a generative AI application engineer. Not just the model. The entire stack:
✅ Agent Orchestration ✅ RAG pipeline ✅ Fine-tuned LLM
✅API ✅ containerization and Kubernetes deployment ✅ CI/CD pipeline
✅ Monitoring and observability that keeps it all healthy at scale.
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WHAT I BUILD FOR YOU
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🔷 RAG & agents — semantic search, reranking, memory, and multi-agent workflows with LangChain, LangGraph, and CrewAI over FAISS, Qdrant, Pinecone, and AstraDB.
🔷 Fine-tuning — adapting open vision-language and language models (LoRA / QLoRA / PEFT / SFT) to your data and domain, efficiently enough to train on accessible hardware.
🔷 Deployment & MLOps — Docker, Kubernetes, FastAPI, GitHub Actions CI/CD, Terraform, and AWS, with Prometheus / Grafana monitoring and LLM-specific tracing via Langfuse. Your AI ships reliably, scales automatically, and is observable from day one.
🔷 Multimodal systems — pipelines that combine text, PDFs, images, and audio using OCR, vision-language models (CLIP, Qwen2-VL, Florence-2, MAIRA-2), and Whisper.
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WHO I WORK BEST WITH
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→ SMBs, funded startups, and agencies who need someone who can both build a GenAI feature and get it deployed — a builder who ships, not just a notebook.
→ CTOs and technical founders who need a senior AI partner, not just a developer
I do not take every project. I take projects where I can make a real difference — where the work is technically meaningful and the client is serious about building something that lasts.
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TECH STACK
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✅ Languages: Python · SQL · Shell · YAML · Rust
✅ LLMs & GenAI: OpenAI GPT · Claude · Gemini · LLaMA · Mistral · Hugging Face
✅ RAG & Agents: LangChain · LangGraph · LlamaIndex · Multimodal RAG
✅ Fine-Tuning: LoRA · QLoRA · SFT · DPO · RLHF
✅ Inference: vLLM · TGI · Quantization (GGUF · AWQ · GPTQ)
✅ Vector DBs: FAISS · ChromaDB · Pinecone · Weaviate. LanceDB
✅ ML/DL: PyTorch · TensorFlow · Scikit-Learn · Keras · OpenCV
✅ Backend: FastAPI · Flask · Django
✅ Databases: PostgreSQL · MySQL
✅ Cloud: AWS (EC2 · EKS · SageMaker · Fargate · ECR)
✅ Containers & Orchestration: Docker · Kubernetes · Helm
✅ Infrastructure as Code: Terraform · Ansible
✅ CI/CD: Jenkins · GitHub Actions · GitLab CI/CD · ArgoCD · CircleCI
✅ Monitoring: Prometheus · Grafana · ELK Stack · Langfuse
✅ Experiment Tracking: MLflow · Weights & Biases · DVC
✅ Version Control: Git · GitHub · GitLab
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If you are building an AI system that needs to actually work in production — not just in a notebook — I would like to hear about it.
Send me a message with what you are building, and I will tell you honestly whether I can help and how.
Steps for completing your project
After purchasing the project, send requirements so Zain can start the project.
Delivery time starts when Zain receives requirements from you.
Zain works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
Client shares data, goals, and preferred recommender type.
Data Preprocessing
Clean, normalize, and transform data for modeling.