You will get Machine Learning, Deep Learning, Data Science, Data visualization
Project details
Are you looking to turn raw data into actionable insights using state-of-the-art machine learning? I offer end-to-end ML solutions, including data preprocessing, feature engineering, model selection, training, tuning, evaluation, and deployment-ready code. Whether you need classification, regression, clustering, or recommendation systems, I build models that are optimized, interpretable, and production-ready.
With hands-on expertise from Machine Learning and real-world project deployments, I use industry best practices and tools like Scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch. I also ensure code reproducibility and provide clear documentation, version control, and consultation.
Perfect for startups, researchers, and product teams looking to implement machine learning workflows that drive measurable impact. Let’s bring your data to life with precision, scalability, and innovation.
With hands-on expertise from Machine Learning and real-world project deployments, I use industry best practices and tools like Scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch. I also ensure code reproducibility and provide clear documentation, version control, and consultation.
Perfect for startups, researchers, and product teams looking to implement machine learning workflows that drive measurable impact. Let’s bring your data to life with precision, scalability, and innovation.
Machine Learning Tools
Apache Spark, Apache Spark MLlib, Azure Machine Learning, BERT, Chainer, ChatGPT, Databricks Platform, Keras, Kubeflow, MATLAB, MLflow, NLTK, NumPy, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, SciPy, SQL, Tableau, TensorFlow, Vertex AIWhat's included
| Service Tiers |
Starter
$300
|
Standard
$1,000
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 20 days |
Number of Revisions | 2 | 3 | 4 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 2 | 4 | 5 |
Number of Graphs/Charts | 2 | 3 | 1 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code |
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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 - 1:14 am local time
⭐️ 56+ AI Solutions delivered
⭐️ 16+ RAG pipelines (including, text, document and multimodal data) deployed
⭐️ 9+ LLMs fine-tuned and shipped to production (LoRA, QLoRA, SFT, DPO, RLHF)
Your AI project is either not in production yet, or it is live but slow, expensive, and fragile under real load.
That is the exact problem I solve.
I am Zain — a Full-Stack AI Engineer with experience in building production-grade AI systems end to end. Not just the model. The entire stack:
✅ Agentic AI System ✅ RAG pipeline ✅ Fine-tuned LLM
✅ Inference serving layer ✅ Kubernetes deployment ✅ CI/CD pipeline
✅ Monitoring and observability that keeps it all healthy at scale.
One recent example: I reduced inference latency by 82% for a production LLM system through GPU-optimized architecture redesign — cutting compute costs significantly while handling 310,000+ monthly AI queries without degradation.
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WHAT I BUILD FOR YOU
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🔷 RAG Pipelines & AI Agents:
Enterprise-grade Retrieval-Augmented Generation systems with semantic search, reranking, memory, and multi-agent orchestration. Built with LangChain, LangGraph, CrewAI, and vector databases including LanceDB, FAISS, ChromaDB, Pinecone, and Weaviate.
🔷 LLM Fine-Tuning & Model Customization:
Custom fine-tuning of open-source LLMs (LLaMA, Mistral, Gemma) using LoRA, QLoRA, SFT, DPO, and RLHF on your proprietary data. 27+ models fine-tuned and deployed to production across healthcare, legal tech, enterprise automation, and SaaS.
🔷 LLMOps & Production Infrastructure:
Full production deployment: Docker, Kubernetes, Helm, CI/CD pipelines (Jenkins, GitHub Actions, GitLab, ArgoCD), Terraform infrastructure-as-code, and real-time monitoring with Prometheus, Grafana, and LLM-specific tracing via Langfuse. Your AI ships reliably, scales automatically, and is observable from day one.
🔷 Inference Optimization & GPU Infrastructure:
vLLM and TGI for high-throughput model serving. Quantization (GGUF, AWQ, GPTQ) for cost reduction. GPU infrastructure management across AWS and specialist GPU providers. I make your inference fast and your cloud bill predictable.
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WHO I WORK BEST WITH
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→ AI startups (Seed to Series B) building their first production LLM product
→ SaaS companies adding AI features to an existing platform
→ Enterprise teams with an AI prototype that needs to become a real product
→ 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.
Client purchases the project and shares requirements
I’ll review your data, use case, and project goals to align the solution with your expectations.
Model Selection & Training
Choose appropriate machine learning algorithms (e.g., regression, classification, clustering). Train the model using proven techniques like cross-validation and hyperparameter tuning.