You will get AI-Powered Vision: Object Detection,Face Verification &Landmark Recognition

Zain U.Status: Offline
Zain U. Zain U.
4.8

Let a pro handle the details

Buy Machine Learning services from Zain, priced and ready to go.
Zain U.Status: Offline
Zain U. Zain U.
4.8

Let a pro handle the details

Buy Machine Learning services from Zain, priced and ready to go.

Project details

Are you looking for a reliable expert to build Computer Vision models that deliver real-world results?
I specialize in developing advanced AI solutions tailored to your needs, including:

✅Object Detection & Localization (YOLO, Faster R-CNN, SSD)
✅Face Recognition & Verification (FaceNet, ArcFace)
✅Landmark Detection (Keypoint Localization)
✅Neural Style Transfer (Artistic Image Generation)
✅Custom CNN Architectures for Image Classification
✅Image Segmentation (U-Net, DeepLab)
✅OCR (Optical Character Recognition)
✅Custom Model Fine-tuning and Transfer Learning

✅ Services Offered: Computer Vision Models | Object Detection | Face Recognition | Deep Learning Solutions
✅ Tools & Frameworks: Python, TensorFlow, Keras, PyTorch, OpenCV, Scikit-learn
✅ Deliverables: Trained model, source code, validation results, deployment-ready package (optional)
✅ Ideal For: Startups, AI Researchers, Businesses, App Developers, Data Scientists
✅ Extra: Post-delivery support and documentation available!

Every project includes thorough validation, clear documentation, and complete source code delivery, committed to offering end-to-end support to help you integrate these solutions into applications.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Apache Spark MLlib, Azure Machine Learning, ChatGPT, Databricks Platform, Databricks MLflow, deeplearn.js, Keras, Kubeflow, MATLAB, MLflow, NLTK, NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlow, Vertex AI, XGBoost
What's included
Service Tiers Starter
$500
Standard
$1,000
Advanced
$2,000
Delivery Time 4 days 7 days 1 day
Number of Revisions
123
Number of Model Variations
123
Number of Scenarios
123
Number of Graphs/Charts
123
Model Validation/Testing
Model Documentation
-
Data Source Connectivity
-
Source Code
4.8
49 reviews
92% Complete
6% Complete
1% Complete
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2% Complete

JS

Joanne Da S.
1.00
Feb 27, 2024
Looking for C, MicroPython developer

AY

Alex Y.
3.85
Sep 18, 2023
Inverter

JS

Joanne Da S.
5.00
May 22, 2023
Looking for C, MicroPython developer Great developer. Actually going to hire again right away

MP

Morgan P.
5.00
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.
5.00
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
Zain U.Status: Offline

About Zain

Zain U.Status: Offline
Senior AI Engineer | RAG | LangChain | AI Agents | Agentic AI | LLMOPs
100% Job Success
4.8  (49 reviews)
Ubauro, Pakistan - 10:37 am local time
⭐️ 1830+ Upwork hours
⭐️ 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
━━━━━━━━━━━━━━━━━━━━━━
✅ 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.

Review the work, release payment, and leave feedback to Zain.