Hire the Best Computer Scientists

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4.8/5
Based on 526 client reviews
Djellab A.

Montreal, Canada

$41/hr
5.0
29 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
  • 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
Muhammad M.

Gujranwala, Pakistan

$15/hr
4.9
177 jobs

With 5+ years of experience and 150+ successful projects, I help businesses build high-performance Computer Vision and AI Agent systems that work in production — not just in theory. 🚀 What I Build ✔ AI Agents & Automation Pipelines (OpenClaw, LangChain, CrewAI, AutoGen) ✔ Semantic Search & RAG Systems using vector databases (FAISS, pgvector, OpenSearch) ✔ Personal AI Assistants with persistent memory & full system access ✔ 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 AI systems (data pipelines → model serving → deployment → monitoring) ✔ LLM and AI agent architectures (RAG, tool use, function calling, multi-agent workflows) ✔ Semantic search and vector database solutions (OpenSearch, FAISS, pgvector) ✔ 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 AI systems (lower latency, reduced cloud costs, improved reliability) 🧠 Core Expertise Computer Vision · AI Agents · OpenClaw · Deep Learning · Machine Learning · Object Detection · Multi-Object Tracking · Image Segmentation · Real-Time AI · Video Analytics · OCR · Data Annotation · Edge AI · Generative AI · LLM Integration · RAG Systems 🛠 Tech Stack AI & Vision: PyTorch · TensorFlow · Keras · OpenCV · MediaPipe · YOLO variants · Faster R-CNN · Vision Transformers AI Agents: OpenClaw · LangChain · CrewAI · AutoGen · RAG · LLMs · GPT-4 · Gemini 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, AI Agent, 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.

  • 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
Emmanuel I.

Abuja, Nigeria

$45/hr
5.0
15 jobs

I build full-stack AI products that actually scale — from React frontends to production LLM pipelines. RESULTS I'VE DELIVERED: → Slashed a FinTech client's GPT-4 bill from $60k to $4.8k/month using semantic caching and prompt optimization → Built a computer vision system monitoring 50+ retail locations with 97% accuracy — $2M in prevented losses → Architected AI-powered financial apps serving thousands of users with voice transcription and custom-trained assistants → Led NASA CAMS visualization portal handling millions of data points in real-time WHAT I BUILD: - Production LLM systems (LangChain, vector databases, RAG pipelines) - Computer vision pipelines (TensorFlow, OpenCV, RTSP streaming) - AI cost optimization (semantic caching, model routing, structured outputs) - Full-stack applications (Next.js, React, FastAPI, Python, AWS) RECENT WORK: - Lead Engineer at RivetAI — AWS, AI-powered film automation - UNICEF — Serverless app handling 5M+ pledges across 5 Nigerian states - NASA CAMS — Celestial data visualization for space research IDEAL CLIENT: You need an AI product built end-to-end, or your current AI system is too slow, too expensive, or doesn't scale. Message me with your challenge. I respond within 24 hours.

  • Python
  • TypeScript
  • API
  • SQL
  • Database Architecture
  • React
  • Node.js
  • Generative AI
  • OpenAI API
  • LangChain
  • Large Language Model
  • Vector Database
  • Computer Vision
  • TensorFlow
  • AI Consulting
Khaled M.

Daqahlah, Egypt

$30/hr
4.9
28 jobs

I'm a Top Rated freelancer and Computer Science graduate who works at the intersection of three fields most people treat separately: Statistics & Machine Learning, Bioinformatics, and AI. That combination is exactly what messy, high-dimensional data needs — the statistical rigor to trust the result, the ML to find the pattern, and the biological context to know what it means. With 3+ years of experience and 24 successful projects (4.98/5 avg. rating), here's what I bring: 📊 Statistics & Machine Learning Rigorous statistical inference and predictive modeling — hypothesis testing, causal inference (Mendelian Randomization), feature engineering, and ensemble models (XGBoost, LightGBM, Random Forest). I don't just build models that score well; I build models you can defend. Example: a coronary artery disease prediction model reaching 0.956 AUC, with feature ablation to prove what actually drives it. 🧬 Bioinformatics & Omics End-to-end analysis of complex biological data — scRNA-seq, RNA-seq, WGS/WES — using reproducible pipelines (Nextflow, Snakemake). From raw reads to normalized matrices to differential expression and biomarker discovery. Example: cut analysis time by 40% on large genomic projects through custom automated pipelines. 🤖 AI & Deep Learning Deep learning frameworks for real diagnostic problems — computer vision, medical image analysis, NLP, and LLM-based tools. ✨ What ties it together Most freelancers do one of these. I connect them — applying AI and ML to biological and clinical data with statistical discipline, then communicating the findings in publication-ready visuals that both scientists and stakeholders can act on. 🛠️ Tech Stack Python (Pandas, Scikit-learn, TensorFlow, PyTorch) · R (Tidyverse, Bioconductor) · Bash · SQL · Linux · Git · Docker · Cloud 💡 Ready to turn your raw data into discoveries you can trust? Let's talk.

  • R
  • Python
  • SQL
  • Data Analysis
  • Data Visualization
  • Machine Learning
  • Bioinformatics
  • Linux
  • Convolutional Neural Network
  • Biostatistics
  • Deep Learning
  • Healthcare
  • Tidyverse
  • TensorFlow
  • Computer Vision
Ahsan I.

Nowshera Kalan, Pakistan

$15/hr
5.0
29 jobs

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
Nidhi B.

Noida, India

$40/hr
4.6
40 jobs

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

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Don't just take our word for it

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.