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Nowshera Kalan, Pakistan
𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐀𝐈 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 | 𝟓𝟎+ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝 I am an AI/ML engineer specializing in end-to-end artificial intelligence development — from concept to deployed, scalable systems that solve real business problems. With 8+ years building machine learning, deep learning, and generative AI solutions for global clients (including Huawei and Turing), I deliver reliable, production-grade systems — not just prototypes. 𝐖𝐡𝐚𝐭 𝐒𝐞𝐭𝐬 𝐌𝐞 𝐀𝐩𝐚𝐫𝐭: ✓ Full-stack AI delivery: requirements → architecture → deployment → maintenance ✓ Battle-tested across 50+ international projects ✓ Focus on business impact, not just technical complexity ✓ Clear communication and rapid iteration cycles 𝐂𝐨𝐫𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: Computer Vision & Image Processing: Object detection | Image classification | ANPR systems | Face recognition | Video analytics | Image segmentation | OCR | Real-time tracking Natural Language Processing & LLMs: Chatbot development | RAG systems | LLM fine-tuning | Text generation | Sentiment analysis | Document summarization | Named entity recognition | GPT/Claude integration Audio & Speech Technologies: Automatic speech recognition (ASR) | Text-to-speech (TTS) | Speaker identification | Audio classification | Voice cloning | Noise reduction 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤:Python | TensorFlow | PyTorch | Keras | Scikit-learn | Hugging Face | LangChain | OpenAI API | FastAPI | Flask | Docker | AWS | Azure 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡:I take full ownership — understanding your use case, recommending the right approach, building the solution, and ensuring it works reliably in production. You get working software, comprehensive documentation, and post-deployment support. 𝐈𝐝𝐞𝐚𝐥 𝐅𝐨𝐫: Custom AI model development and training ML pipeline design and automation API development and third-party integration Proof-of-concept to production scaling Legacy system modernization with AI Let's discuss how AI can create measurable value for your business. Message me with your project details, and I'll respond with relevant examples and a clear path forward.
- Computer Science
- Python
- Artificial Intelligence
- Image Processing
- Machine Learning Model
- Flask
- Neural Network
- PyTorch
- Deep Neural Network
- Machine Learning
- Apache MXNet
- Convolutional Neural Network
- Computer Vision
- API Development
- TensorFlow
Montreal, Canada
I build AI agents, automations, and RAG systems that ship to production - not demos that die in a notebook. As the founder of BeautyBuzz AI (a live SaaS with real users), I've taken AI products through the full cycle: idea → build → deploy → scale. I'm a full-stack developer with deep AI expertise, which means you get one person who can design the model, build the backend, and ship the app - no handoffs, no gaps. What I build for clients: • AI Agents & Multi-Agent Systems — LangChain, LangGraph, CrewAI; tool-calling, memory, orchestration • AI Automation & Workflows — n8n, Make, Zapier + OpenAI/Claude; connect your CRM, email, Slack, docs so work runs itself • RAG & Knowledge Assistants — chat over your documents/data with accurate, cited answers (vector DBs, hybrid retrieval) • LLM Fine-Tuning & Integration — OpenAI, Anthropic Claude, Gemini, Llama; prompt engineering, evals, cost/latency optimization • Full-Stack AI Products & MVPs — Python/FastAPI backends, clean databases, cloud deployment (AWS/GCP/Azure), Docker, CI/CD Why clients hire me: ✅ 100% Job Success Score and $50K+ earned on Upwork ✅ Founder of a real, deployed AI SaaS — I think about your business outcome, not just the code ✅ Recent 5-star work: agentic RAG systems, document-processing automation, a high-performance LLM inference engine, and a 150-hour PostgreSQL + Python app Tech I work with daily: Python, FastAPI, LangChain, LangGraph, OpenAI & Claude APIs, MCP, Pinecone/pgvector, Hugging Face, PyTorch, TensorFlow, Docker, AWS, n8n, SQL, React/Next.js. If you need an AI agent, an automation that saves hours every week, or a production-ready AI feature built properly the first time, send me a message or invite me to your job - I reply within hours and I'll tell you honestly what's worth building.
- Python
- Recommendation System
- Natural Language Processing
- Deep Learning
- Computer Vision
- Artificial Intelligence
- Machine Learning
- AI Agent Development
- AI App Development
- LangChain
- Retrieval Augmented Generation
- Large Language Model
- Generative AI
- AI Chatbot
- Automation
- OpenAI API
- Claude
- FastAPI
- PostgreSQL
- Data Science
Gujranwala, Pakistan
Computer Vision Expert | YOLO | Object Detection | Tracking | OpenCV | Deep Learning | Jetson | Real-Time Systems | Image/Video Labeling Specialist | Machine Learning | OCR With 5+ years of experience and 150+ successful projects, I help businesses build high-performance Computer Vision and Deep Learning systems for real-world applications. I specialize in Object Detection, Multi-Object Tracking, Image Segmentation, and Real-Time Video Analytics, delivering scalable AI solutions used in production environments. 🚀 What I Can Do For You ✔ Build Object Detection systems (YOLOv8, YOLO11, YOLO26) ✔ Develop Multi-Object Tracking (DeepSORT, ByteTrack, BOT-SORT) ✔ Create Real-Time Video Analytics pipelines ✔ Design Image Segmentation models (U-Net, DeepLabV3+) ✔ Develop Face Recognition & Liveness Detection systems ✔ Build OCR & Document AI solutions ✔ Optimize models using TensorRT, CUDA, GPU acceleration ✔ Deploy AI systems via APIs, Docker, Cloud (AWS), Jetson ✔ Provide high-quality Image & Video Annotation / Labeling 👁 Core Expertise • Computer Vision • Deep Learning • Machine Learning • Object Detection • Image Segmentation • Multi-Object Tracking • OpenCV • YOLO (YOLOv8, YOLOv11, YOLOv26) • Real-Time AI Systems • Video Processing • OCR & Document AI • Data Annotation & Labeling 🧠 Real-World Solutions I Build • Surveillance & Smart Monitoring Systems • Retail Analytics & Customer Tracking • Face Recognition & Identity Verification • Industrial Defect Detection • Medical Image Analysis • Traffic & Vehicle Detection Systems ⚡ End-to-End Development I handle complete AI pipelines: Data Collection → Annotation → Model Training → Optimization → Deployment You get a fully production-ready system, not just a model. 🛠 Tech Stack 🔹 Deep Learning PyTorch, TensorFlow, Keras, CNN architectures, YOLO variants 🔹 Computer Vision OpenCV, MediaPipe, OCR systems, real-time video processing, detection and tracking pipelines 🔹 Machine Learning Scikit-learn, XGBoost, classification, regression, clustering 🔹 Tracking & Optimization DeepSORT, ByteTrack, SORT, TensorRT, CUDA 🔹 Backend & Deployment FastAPI, Flask, Docker, AWS, Jetson 🔹 Languages Python, C++ 💡 Why Clients Hire Me ✔ 150+ successful projects ✔ 100% Job Success (Top Rated) ✔ Real-time, high-performance systems ✔ Scalable & production-ready solutions ✔ Strong optimization (FPS, latency, memory) ✔ Clear communication & fast delivery 📌 Quick Overview 150+ Projects • 100% Job Success • Top Rated 🎯 Computer Vision — YOLOv8/11/26, Faster R-CNN, U-Net 📍 Tracking — DeepSORT, ByteTrack, BOT-SORT ⚡ Real-Time AI — OpenCV, PyTorch, TensorFlow 🧠 Deep Learning — CNNs, Vision Transformers 📄 OCR & Document AI — Tesseract, Google Document AI 🚀 Deployment — TensorRT, CUDA, Docker, AWS, Jetson 📩 Call to Action Looking to build a Computer Vision system, Object Detection model, or Real-Time AI solution? 👉 Send me a message — I’ll help you design the best approach and deliver a scalable, production-ready solution.
- Computer Vision
- Object Detection & Tracking
- YOLO
- OpenCV
- Deep Learning
- Image Annotation
- Convolutional Neural Network
- Image Segmentation
- Semantic Segmentation
- Anomaly Detection
- AI Model Integration
- NVIDIA Jetson
- Generative AI
- Large Language Model
- Retrieval Augmented Generation
- OCR Algorithm
- Python
- Artificial Intelligence
- Machine Learning
- Data Annotation
Noida, India
I am a Data Scientist, AI, Machine Learning, Software Engineer, and certified SEO Content Writer with 15 years of professional experience building software at any scale. I have expertise in Python, R, and SQL programming, and I love to experiment with data and write about it. I am proficient in: ✅ AI Automation Projects: AI Agents ● Agno (Phidata) ● Zapier ● GrogCloud ✅ Data Science and Machine Learning: ● Regression and Classification Models ● Ensemble models ● Data Visualisation with Python and Power BI. ✅ Natural Language Processing (NLP): ● BOW, TF-IDF, W2Vec, Glove Embeddings ● Text classification (including Sentiment Analysis) ● Named Entity Recognition (NER, automatic extraction of relevant entities, like names, locations, dates, etc.) ● RNNs and LSTMs ✅ Computer Vision (CV): ● Image Classification ● Object Detection (OpenCV, YOLO) ● Instance segmentation / Semantic Segmentation (Mask R-CNN) ● Optical Character Recognition (OCR) and different Computer Vision techniques like CNN, RCNN, Fast RCNN, Faster R-CNN, Mask-R CNN. ✅ Data Analyst (Power BI) ● Experienced Data Analyst with hands-on experience with Power Query Editor and DAX Queries in Power BI. ● Data Analyst, Sales Analyst, Finance Analyst, and Supply Chain Analyst in Power BI. ● Expert in creating various Dashboards and analyzing in detail. ● Importing data from multiple sources and exporting the dashboard to the Power BI Service. ✅ I am a certified SEO content writer and Data Scientist. ✅ Technical Skills ● Python, R, TensorFlow, Keras, PyTorch, Pandas, NumPy, Seaborn, Matplotlib, Plotly, OpenCV, YOLO, BERT, Transformer, NLTK, Mask-RCNN, Google Docs, Microsoft Excel, Microsoft Office, MySQL, Power BI, Java, J2E, Spring, Hibernate. ✅ My website: datasciencehorizon I am committed to delivering high-quality results, and my professional expertise ensures that I can bring a comprehensive and insightful approach to your project. Please review my portfolio and my work history for testimonials from previous clients. Thank you for considering my profile. I look forward to collaborating with you soon.
- Python
- Computer Vision
- Natural Language Processing
- Python Scikit-Learn
- Data Science
- Machine Learning
- SQL
- Content Writing
- Artificial Intelligence
- Technical Writing
- Content Creation
- Content SEO
- SQLite
- MongoDB
- Data Mining
Muzaffargarh, Pakistan
I am junior data scientist and computer vision expert having 3 years of research experience during the research work of MS. Have good expertise in Python environment and data science domain. Have good understanding of Various AI techniques like Image classification, Image segmentation, prediction and clustering of data. Have good skill set in Data science libraries like Scikit-learn and Keras. Have good experience in computer vision and performed various computer vision tasks like features extraction as well as features matching using OpenCV.
- Python Scikit-Learn
- OpenCV
- Keras
- SciPy
- Machine Learning
- TensorFlow
- Computer Vision
- YOLO
- Generative AI
- Ecommerce Website Development
- WooCommerce
- Shopify
- WordPress
- Digital Marketing
- Python
Cairo, Egypt
I am an educated data scientist & ML engineer who has worked in academia teaching Algorithms, machine learning and AI. I've been programming in python daily for over 3 years and that's where I do most of my data analysis. I have a lot of experience with machine learning and data visualizations, mainly in python. Working in a diverse set of fields has given me experience with many different types of data and obtaining it from many different sources. Some of the projects I have done are: • Computer Vision • Regression modeling • Machine learning and various statistical models in big data • Computer vision • Predictive Analytics • Natural Language Processing Tech stack: Keras, Python, SQL, TensorFlow, PyTorch, Scikit-learn, Spark, PennyLane
- C++
- Python
- PyTorch
- Data Science
- LaTeX
- C#
- .NET Framework
- Node.js
- PHP
- Software Design
- Research Papers
- NoSQL Database
- Java
- NumPy
- GitHub
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Computer Science vs. Computer Engineering: What's the Difference?
As technology evolves and spins off into highly specialized fields, so do the careers and advanced degrees that support it. As these degrees and specialties increasingly narrow their areas of focus, it can be helpful to understand how they play into the larger technology landscape by breaking them down into two core curriculum: computer science and computer engineering. And while there’s common ground between them, knowing where these two fields both overlap and diverge is a good place to start.
So, how are they different, and where does software engineering come in? Whether you’re interested in studying one or the other, or you’re just unsure how the two fields differ, here’s a boiled-down look at computer science vs. computer engineering.
Note: If you’re a student or professional looking to enter one field or the other, there will be a good bit of overlap between the two, with certain concepts and processes playing a role in both. Ultimately, they’re both concerned with enabling computers to read, write and use data properly to accomplish something, so there will be commonalities across the board.
The Theoretical: Computer Science
Computer science is primarily concerned with computational theory, namely the architecture, data, algorithms, and programming languages that comprise the software that’s run on a computer. Computer scientists are focused on things like code, algorithms, artificial intelligence, database design, and software design.
A computer scientist will code the instructions, protocols, and operating systems that run on top of hardware—a very generalized way of describing this incredibly varied field.
The Practical: Computer Engineering
Computer engineering takes that theory and applies to real life. Essentially it’s computer science put into action, married up with the field of electrical engineering. If computer science happens in code, in the abstract, computer engineering often happens in the lab. It involves designing and prototyping the tiny circuits and processing units that bridge the computer’s hardware components with the software it’s running—whether the implementations are embedded systems, microprocessors, networked IoT devices, or “smart” anything.
Computer engineering puts the theories of software design and data processing into action on a granular level. Think semiconductors and printed circuit boards, and the electrical integrations between all of these components.
A computer engineer will concentrate on how the software created by a computer scientist will get mapped out and run on the device. They’ll touch many different components: electrical engineering, hardware design, software design, and how each of these interoperates with the others.
Where Both Ends Meet: Software Engineering
You can’t talk about computer science and computer engineering without touching on software engineering—the bridge between the two that provides the architecture for the instructions the hardware executes.
So where does software engineering come into the mix? While computer scientists focus on the theories and algorithms and computer engineers focus on the hardware implementations, a software engineer bridges both disciplines together, applying computer science theories to software. A software engineer gets even more hands-on with programming by translating those concepts into functional applications that leverage the hardware they run on.
Studying the Disciplines: Computer Science Degrees vs. Computer Engineering Degrees
How is a CompSci degree different from a CompE degree? In the simplest of terms, computer scientists study theory and computer engineers build the things that bring those theories to life. Inside these disciplines, there are bound to be very specialized degrees, but knowing the basic differences will help you get started.
Both degrees will study basic computer operation, mathematics, and programming, but beyond that they’ll go on to emphasize different things. CompSci tends to be more theoretical while CompE is more practical.
A CompE degree will probably include a good amount of computer science coursework, but not vice versa—a CompSci student won’t get into the nuts and bolts of electrical circuits and engineering. If you’re studying computer science, expect to cover everything from operating systems and computer graphics to numerical methods and computational theories. If you’re studying CompE, you’ll likely cover similar areas of math and science, but also more physical studies like electronics, circuits, robotics, sensors, and networking.
Beyond education: A real world example
To get an idea of how these interact, take any “smart” thing as an example. A smartphone, smart car, smart thermostat, or even a smart toothbrush—anything electronic that has an embedded computer system to make it run. Both disciplines have to come together to make this smart object a reality.
In a smart car with touchscreen navigation, for example, a computer engineer will design the computer systems: the internal workings like the chips, microprocessors, and circuit boards, and the components like the screen, buttons, and menus the user interacts with. The software engineer then uses computer science theories to write the car’s operating system, the programs, applications like Pandora or a tire pressure monitoring system, and any network communications (say, how the car’s GPS communicates with nearby towers).
In Summary
What kind of work do you want to do? A good question to ask is how close to the actual hardware do you want your computing work to be? Professionals working with software that’s closer to the hardware—cell phones, calculators, smart devices, etc.—will have more of that granular engineering experience. But more high-level software design that isn’t as concerned with interfacing with the hardware—because it’s designed to run on an operating system like Windows or Linux—would be more of a computer science degree.
The key is where the two intersect—and how software engineering comes into play—and having a holistic understanding that’s more conducive to building better integrated systems into modern, networked devices.
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