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Jamie Eduardo R.

Digos, Philippines

$10/hr
5.0
3 jobs

Memory updated Here's a brief profile overview: Jamie Eduardo Rosal Data Scientist | System Developer | AI Engineer | Software Engineer I am a versatile technology professional with a strong foundation in data science, system development, artificial intelligence, and software engineering. My expertise lies in building robust systems, crafting intelligent solutions, and driving innovation through cutting-edge technologies. I have successfully led projects from conception to deployment, delivering impactful results and optimizing processes across various domains. My strengths include analytical thinking, problem-solving, and a deep understanding of machine learning algorithms and software development practices. I hold a solid educational background, continuously upgrading my skills to stay at the forefront of the industry. Key achievements include developing advanced AI models for predictive analytics, implementing scalable software solutions, and contributing to research in emerging technologies.

  • JavaFX
  • Data Science
  • MySQL
  • Machine Vision
  • Machine Learning
Dr Ijaz U.

Peshawar, Pakistan

$45/hr
5.0
89 jobs

Your paper deserves to be published. Your thesis deserves to pass. Your grants and NSF proposal deserve funding. I'm Dr. Ijaz, a PhD in Computer Science (University of Nottingham) with hands-on experience as a peer reviewer for Wiley and IEEE journals. I know exactly what editors and reviewers look for, and win grants like NSF — with PhD-level precision from someone who has been on both sides of the reviewer's desk. I don't do generic editing and proofreading. I do PhD-level, reviewer-minded editing that gets your work across the finish line. ✔ 100% Job Success · Top Rated · $30K+ earned on Upwork ✔ 78 completed projects · Clients across 4 continents ✔ Avg. response time: under 4 hours ✔ PhD in Computer Science (University of Nottingham) ✔ IEEE / Wiley reviewer experience ✔ 100% Job Success | Top Rated ⭐ Who I Work With Researchers who are serious about publication. PhD and MS students, university faculty, postdocs, and funded research labs working under real deadlines. Faculty and scientists are looking for funding from NSF and DoD. If you've received reviewer comments that feel impossible to address, a thesis your supervisor keeps sending back, or an NSF proposal that needs to be airtight — this is where I can help. ⭐ About Me I am a PhD in Computer Science with extensive experience in academic editing, research consulting, and grant proposal development. I have successfully supported: Q1 journal publications MS, PhD theses, and dissertations NSF and international grant proposals Reviewer-response and resubmission cycles I work with precision, ethics, and a reviewer’s mindset — not generic proofreading. 🔬 What I Do ✔ Thesis & Dissertation Editing: I work through supervisor feedback systematically — fixing logic gaps, argument flow, grammar, and structure. Fluent in LaTeX, Overleaf, and Word. ✔ Journal Paper Proofreading & Revision: I align your paper with IEEE, Elsevier, and Springer standards, address reviewer comments point by point, and strengthen your technical clarity before resubmission. ✔ Research Paper Polishing: Sharper arguments, stronger novelty positioning, cleaner references (Zotero / EndNote / Mendeley). I make your contribution impossible to overlook. ✔ NSF & Grant Proposal Writing: From technical narrative to budget justification — I write proposals that reviewers fund, not file away. Experience with NSF and international funding bodies. ✔ Formatting & Conversion: APA / IEEE / Springer templates, LaTeX ↔ Word/PDF conversion, journal submission formatting — done right the first time. 🎯 Why Researchers Choose Me Over Generic Editors Most editors fix grammar. I fix the argument. I bring a reviewer's eye to every document, catching not just language issues but also logical gaps, weak justifications, and structural problems that lead to desk rejections. My background in Computer Science, AI, and Smart Systems means I understand your content, not just your sentences. ✔ Confidential & ethical with strong and ethical use of AI, no plagiarism, ever ✔ On-time delivery, every time ✔ You get a collaborator, not just a contractor 📩 Next Step Message me with: Target journal or funding call Deadline Current draft (if available) I’ll respond with a clear plan and timeline in ln a matter of hours.

  • Computer Science
  • Academic Proofreading
  • Fact-Checking
  • Research Papers
  • Proofreading
  • Formatting
  • Education
  • Scientific Research
  • Academic Research
  • Academic Editing
  • LaTeX
  • Research Proposals
  • Professional Journal Citations
  • Machine Learning
  • References & Citations
Djellab A.

Montreal, Canada

$41/hr
5.0
30 jobs

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 $60K+ 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
  • Machine Learning
  • AI Agent Development
  • AI App Development
  • Data Science
  • Artificial Intelligence
  • LangChain
  • Retrieval Augmented Generation
  • Large Language Model
  • Generative AI
  • AI Chatbot
  • Automation
  • OpenAI API
  • Claude
  • FastAPI
  • PostgreSQL
Muhammad M.

Gujranwala, Pakistan

$50/hr
4.9
178 jobs

With 5+ years of experience and 150+ successful projects, I help businesses build high-performance Computer Vision systems that work in production — not just in theory. 🚀 What I Build ✔ Object Detection & Multi-Object Tracking (YOLO26, YOLOv12, YOLO11, YOLOv8, DeepSORT, ByteTrack, BOT-SORT) ✔ Real-Time Video Analytics & Surveillance Systems ✔ Face Recognition & Liveness Detection ✔ Image Segmentation (U-Net, DeepLabV3+, Semantic & Instance) ✔ OCR & Document AI (Tesseract, Google Document AI, PaddleOCR) ✔ Industrial Defect Detection & Quality Control ✔ Medical Image Analysis ✔ Traffic & Vehicle Detection Systems ✔ Retail Analytics & Customer Behavior Tracking ✔ Edge AI Deployment (Jetson, TensorRT, CUDA, Docker, AWS) ✔ Model Optimization (FPS, latency, memory efficiency) ⚡ What I Deliver ✔ End-to-end computer vision systems (data pipelines → model serving → deployment → monitoring) ✔ Real-time computer vision systems (detection, classification, tracking, segmentation) ✔ Custom YOLO model training on your own dataset (YOLOv8, YOLO11, YOLO26) ✔ Multi-camera surveillance & smart monitoring systems ✔ Video analytics pipelines with real-time alerting & reporting ✔ Scalable AI infrastructure on AWS (SageMaker, EKS, Lambda, EC2) ✔ Production-grade APIs and backend services ✔ Optimization of existing systems (lower latency, reduced cloud costs, improved reliability) 🧠 Core Expertise Computer Vision · Deep Learning · Machine Learning · Object Detection · Multi-Object Tracking · Image Segmentation · Real-Time AI · Video Analytics · OCR · Data Annotation · Edge AI 🛠 Tech Stack AI & Vision: PyTorch · TensorFlow · Keras · OpenCV · MediaPipe · YOLO variants · Faster R-CNN · Vision Transformers Tracking & Optimization: DeepSORT · ByteTrack · BOT-SORT · TensorRT · CUDA Backend & Deployment: FastAPI · Flask · Docker · AWS · Jetson · REST APIs 🌍 Industries I Serve Retail · Security & Surveillance · Healthcare & Medical · Industrial & Manufacturing · Traffic Management · Smart Cities · Agriculture · Sports Analytics 💡 Why 150+ Clients Chose Me ✔ 100% Job Success Score — Top Rated on Upwork ✔ 5+ years delivering real-world AI systems ✔ Production-ready, scalable solutions ✔ Strong optimization — high FPS, low latency ✔ Clear communication & on-time delivery 📩 Let's Work Together Looking to build a Computer Vision system, Object Detection model, or Real-Time AI solution? 👉 Message me now — I'll help you design the best approach and deliver a scalable, production-ready solution fast. /// The following is just for SEO. Please ignore it /// #computer vision #computer vision engineer #computer vision opencv #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning #ocr #computervision #machinelearning #ai # ml #cv #ai #transformers #llm #generative ai #generativeai #opencv #jetson nano #jetsonnano #nvidia #model training #modeltraining #yolo #object detection #objectdetection #model training #modeltraining #training yolo model #trainingyolomodel #objecttracking #object tracking #image annotation #imageannotation #datasetannotation #dataset labelling #datasetlabelling #dataset

  • Computer Vision
  • Object Detection & Tracking
  • YOLO
  • OpenCV
  • Deep Learning
  • Convolutional Neural Network
  • Image Segmentation
  • Anomaly Detection
  • AI Model Integration
  • NVIDIA Jetson
  • Generative AI
  • Large Language Model
  • Retrieval Augmented Generation
  • OCR Algorithm
  • Python
  • Artificial Intelligence
  • Machine Learning
  • AI Chatbot
  • AI Agent Development
  • AI Development
Hwei Geok N.

Duesseldorf, Germany

$150/hr
5.0
24 jobs

I'm an Upwork Expert-Vetted Data Scientist who covers what usually takes two specialists: classic ML and modern LLM systems, shipped to production. Nobody understands your business like you do, and you know exactly the bottleneck you want gone. You want someone who can see it just as clearly, bring it to life the way you imagined, and build it to last. That has been my track record. Imagine handing off your idea, and what comes back makes you say, "This is exactly what I had in mind!" That is the feedback I hear most from my clients. Here's what working together looks like: You explain the vision. I turn it into a clear plan, build the system end-to-end, and ship something your team can actually run and trust. Whether it's an LLM or RAG application, an AI agent, or a forecasting or anomaly-detection engine, it is handed over to you already deployed in production, documented, and yours to keep building on. What my clients say: → "She took our vision as her own and brought it to life." → "When she says she'll handle something, it's done." → "Hwei is the proverbial needle in a haystack. She gets it. She gets it done." Recent projects: • A US women's health startup: I led the AI intelligence layer behind their production multi-agent chatbot, live ahead of their fundraise. • A Fortune 500 consumer brand: I built a shelf-life forecasting platform that turned weeks of manual modeling into 30-second predictions. • An Italian telecom firm: I built an LLM document validation pipeline that cut compliance review from days to minutes, with zero false approvals against their own 99.7% precision target. • A Dutch water consortium: I built an anomaly-detection system that caught real contamination with zero false alarms, then grew it into a 12.3M-measurement data platform that their own team can run without a developer. → Expert-Vetted · Top 1% on Upwork · 100% Job Success → ML and AI since 2018 · 8 published papers · Trained at Fraunhofer SCAI and the University of Hamburg Tell me what you're trying to build, and I'll reply with a clear path forward, built around your goals. ================== Tools and capabilities: • Generative AI and LLMs: large language models, RAG (Retrieval Augmented Generation), AI agents and multi-agent systems, chatbots, prompt engineering, LLM evaluation, vector databases, LangChain, Pydantic AI • Machine learning: predictive modeling, anomaly detection, time series forecasting, predictive maintenance, statistical analysis, deep learning, computer vision, natural language processing (NLP) • Engineering and delivery: Python, FastAPI, Docker, cloud deployment (AWS, Azure, Render), data analysis, data pipelines, interactive dashboards (R Shiny, Streamlit, Gradio), data visualization • Model APIs: OpenAI, Anthropic Claude, Google Gemini, Hugging Face • Consulting: artificial intelligence (AI) strategy, AI implementation, AI consulting, code audits, knowledge transfer

  • Large Language Model
  • Retrieval Augmented Generation
  • AI Agent Development
  • Generative AI
  • Prompt Engineering
  • Machine Learning
  • Artificial Intelligence
  • Anomaly Detection
  • Time Series Forecasting
  • Predictive Analytics
  • Data Engineering
  • LangChain
  • Computer Vision
  • Statistical Analysis
  • Chatbot Development
  • Data Visualization
  • Python
  • Data Science
  • AI Consulting
  • AI Implementation
Alfonso A.

Cavite City, Philippines

$5/hr
4.0
3 jobs

During my five years of experience in data annotation, I have developed a deep expertise in image annotation for various applications, including object detection, image classification, semantic segmentation, and more. Here are some highlights from my portfolio: Object Detection: I have annotated thousands of images for object detection tasks, including bounding box annotations, landmark annotations, and more. Some of the projects I have worked on include identifying vehicles in traffic, recognizing pedestrians in surveillance footage, and detecting defects in manufacturing processes. Image Classification: I have annotated images for various types of image classification tasks, including binary classification, multi-class classification, and hierarchical classification. Some of the projects I have worked on include identifying different species of animals, classifying products for e-commerce platforms, and recognizing different types of food. Semantic Segmentation: I have annotated images for semantic segmentation tasks, which involves labeling each pixel in an image with a corresponding class label. Some of the projects I have worked on include segmenting medical images for diagnostic purposes, identifying different land cover types in satellite imagery, and recognizing different types of objects in aerial imagery. Throughout these projects, I have consistently demonstrated a high level of accuracy, attention to detail, and ability to work under tight deadlines. My experience in image annotation, combined with my knowledge of annotation tools and techniques, makes me a valuable asset to any team working on computer vision projects.

  • Computer
  • Video Editing
  • Video Post-Editing
  • Machine Learning
  • Computer Vision
  • Adobe Photoshop
  • Video Editing & Production
  • Communications
  • Microsoft Excel
  • Computing & Networking
  • Data Processing
  • Artificial Intelligence
  • Hardware Troubleshooting
  • Computer Hardware
  • Microsoft Office

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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.