Hire the Best Computer Vision Engineers

The hardest AI problems don't have an off-the-shelf solution. That's exactly where I work. When I start a project, the first thing I do is understand your business. Not the technical requirements. The business. What's generating revenue, what's blocking it, and what, if built or fixed, would move the needle most. Only after that do we decide what to build and in what order. I rank everything by business impact. The problems that cost you the most by not being solved go first, everything else follows. That's not how engineers typically approach a project. They wait for a task list and execute it well. What I bring is different. I'm a technical partner who understands your business well enough to help you decide what's worth building, and goes deep enough technically to build it right. That combination changes the kind of outcome you get. The best way to show what that looks like is through the work itself. Wildfire Spread Prediction A client needed an AI model to predict how wildfires would spread the following day. The existing research benchmark was accurate but expensive, the kind of system that needs serious, costly hardware to run in production. I questioned the assumption that accuracy required that scale. By redesigning the architecture and optimization strategy from the ground up, I built a model 77% smaller than the benchmark that simultaneously improved accuracy across every metric by over 13%. Smaller, cheaper to run, faster to deploy, and more accurate than what it replaced. The result was a production-ready system that didn't come with an infrastructure bill that made it impractical to actually use. AI Evaluation Platform: Fairness Redesign A client was building a competition platform where participants submitted answers to be judged and ranked on a leaderboard. The system worked. But after building it, I noticed a problem the client hadn't seen yet. Different judges have different standards. One strict judge evaluating one participant and a lenient judge evaluating another meant two equally talented people could get completely different scores, not because of their performance, but because of who happened to review them. The leaderboard would be unfair by design, and nobody would know why. I brought the problem to the client along with a solution. Instead of assigning each participant to a single judge, distribute individual questions across multiple judges and normalize the scores. Every participant now gets evaluated against the same standard regardless of who reviews them. The client didn't ask for this. But a technical partner catches what a task executor doesn't, and catching it before launch protected something the client had spent months building. That's what I mean by partnership. Here's where I apply it: Computer Vision: if it needs to see, detect, segment, or interpret visual data in real time, I build it. LLM Fine-Tuning: if a generic model doesn't understand your domain, I train one that does. Geospatial AI: if your business runs on location data, satellite imagery, or physical infrastructure, I turn it into actionable intelligence. Who this is for If someone has already told you your AI problem is too complex, too specific, or technically not feasible, I'm probably the right person to talk to. I don't work on standard problems. Not because simpler work isn't valuable, but because my mission is specifically to go where current AI hasn't been. To build systems that are fully custom to your exact needs, optimized to the highest degree of efficiency the technology allows, and capable of functioning in conditions where generic solutions have already failed. Model optimization isn't an afterthought in my work. It's a core part of how I think about every system I build. Smaller, faster, more efficient, and more accurate is always the goal. The projects I take on are chosen by one filter. Does this push something forward? Does it solve a problem with real consequences, scientifically, operationally, or for the people and businesses it affects? If yes, then cost is a conversation we have together, not a barrier that stops us before we start. If the problem is straightforward and well-solved, I'll tell you honestly and point you in the right direction. What drives this isn't a business model. It's a belief that the boundary of what AI can do in the real world is still far from where it should be, and that the most valuable thing I can do is work on the problems that move it forward. If that sounds like your project, send me a message describing the problem. Not the technical requirements. The problem. We'll figure out the rest together. Computer vision, object detection, real-time vision, image segmentation, visual AI, vision systems, production vision, perception systems, LiDAR, sensor fusion, autonomous vehicles, 3D object detection, autonomous navigation, CARLA simulation, autonomous systems, LLM fine-tuning, custom LLM, LoRA, QLoRA, data training, fine tuning, AI training

Most Machine Vision projects fail between the prototype and production. I've shipped 54+ that didn't. ⚙️YOLO Detection | Pose Estimation | Object Tracking | AI Agents | LLM Integration Sports & Fitness AI | CCTV & Surveillance AI | Retail AI | Healthcare AI You have a working concept... or a clear problem involving cameras, video, or image data. The challenge is making it fast, accurate, and stable under real-world conditions. Wrong framework choices. Inference too slow for live video. Models that break the moment lighting, angle, or environment changes. And systems that detect things but can't reason about them or act on them autonomously. That's exactly where most builds stall. I design and build real-time computer vision pipelines that go all the way... from model training to live deployment... and increasingly, from visual perception to autonomous AI agents that understand, decide, and narrate. LLM APIs (OpenAI, GPT-4o, Gemini, Claude) | AWS (EC2, S3, Lambda) | Azure Cloud Services | MLOps & API Integration | Model Deployment & Scaling While most CV engineers stop at training the model, I go further: → High-speed inference optimization using TensorRT, ONNX, OpenVINO, FP16/INT8 (up to 5× faster) → LLM agents integrated with vision pipelines for alerts, reasoning, and automation → Mobile AI deployment using Core ML (iOS) and TFLite (Android) with 10+ shipped apps → Edge AI deployment on Jetson, OpenVINO, CUDA, and embedded systems → End-to-end pipelines: data → training → optimization → real-time deployment Key Accomplishments: ⭐ $5M+ revenue from AI solutions ⭐ 100+ computer vision systems delivered ⭐ Built and launched 2 SaaS products ⭐ Real-time sports AI (7+ sports, 15+ teams) ⭐ 10+ mobile AI apps (iOS Core ML, Android TFLite) ⭐ Production AI for surveillance, industrial & safety use cases ⭐ Medical imaging AI deployed in 5+ hospitals ⭐ Up to 5× faster inference (ONNX, TensorRT, FP16/INT8) ⭐ Large-scale tracking & re-ID (1M+ labeled data) ⭐ Agentic AI systems for autonomous decision-making If you have read this far, please note that I appreciate you taking the time to learn about me. Personally, it’s been an amazing journey and knowledge exercise to get to this level of competence in AI and software development. Domain Expertise: ✅ athlete tracking | shot detection | scoring | drill analysis | pose estimation ✅ defect inspection | PPE compliance | staff monitoring | meter reading | quality control ✅ ANPR | crowd monitoring | people counting | intrusion detection | perimeter security ✅ tumor detection | ultrasound | X-ray/CT analysis | lesion segmentation | medical imaging ✅ aerial monitoring | traffic flow | license plate recognition | vehicle & accident detection ✅ customer analytics | receipt extraction | shelf monitoring | inventory tracking Tech Stack: YOLOv5–YOLOv8–YOLOv11, Detectron2, MMDetection, DeepSORT, StrongSORT, MediaPipe, OpenPose, Pose Estimation, Action Recognition, Segmentation (semantic & instance), OCR, anomaly detection, object tracking, PyTorch, TensorFlow, TFLite, Core ML, OpenCV, FastAPI, Flask, ONNX, TensorRT, OpenVINO, CUDA, AWS, Azure, GCP, edge AI, mobile AI, real-time inference, video analytics, AI automation, LLM integration (GPT-4o, Claude, Gemini, Groq), LangChain, LangGraph, CrewAI, RAG systems. 💬 If your project involves cameras, video, or images... and you need it fast, accurate, fully deployed, and intelligent enough to reason and act autonomously... I am the engineer you are looking for.

⭐️Top Rated Plus — Top 1% on Upwork ⭐️ Over 100 Enterprise LLM and Computer Vision Solutions Delivered 💸 $5 Million+ Generated in revenue for top companies worldwide 🥇Gold Medalist in Computer Engineering & Microsoft Imagine Cup Winner I’m a Senior Computer Vision and AI, and Full-Stack Developer with 8+ years of experience building production-grade AI systems, Large Language Model (LLM) solutions, intelligent chatbots, and computer vision applications for startups and enterprises worldwide. I’ve helped 90+ companies across the US, Europe, and the Middle East launch scalable AI products and generate over $5M in revenue through automation, predictive systems, and generative AI. Currently, I lead Aeyron Technologies Pvt. Ltd. as CEO while delivering high-impact freelance AI solutions for global clients. If you’re looking for someone who can design, build, fine-tune, and deploy real AI systems and not demos, you’re in the right place. LLMs, Generative AI & Chatbots: • LLM fine-tuning and custom model training (LLaMA, LLaMA-2, BLOOM, OPT) • Prompt engineering and workflow optimization • RAG systems using LangChain, FAISS, ChromaDB • AI chatbot development and automation agents • API-based AI integrations for web and mobile applications Common use cases: AI assistants, document intelligence, knowledge-base bots, SaaS AI features, automated workflows. Computer Vision Expertise: • Object detection and tracking (YOLO, OpenCV, custom deep learning models) • OCR systems and document processing • Face recognition and biometric systems • Image segmentation and analytics • Video intelligence pipelines • 3D vision and stereo reconstruction • AR/VR vision-based applications Machine Learning & AI Engineering: • Predictive modeling and forecasting • NLP systems and text analytics • Time-series analysis • Reinforcement learning • Data pipelines and MLOps • AI automation tools • AI-powered dashboards and products Full-Stack & AI Product Development: Frontend: React, Angular, Flutter, Streamlit, Tailwind, Figma Backend: Node.js, Django, Flask, .NET, REST APIs Databases: MongoDB, PostgreSQL, MySQL, Firebase, SQL Server Cloud & DevOps: AWS, GCP, Azure, Docker, Kubernetes, Nginx, Heroku I deliver end-to-end AI products from MVP to enterprise scale. AI/ML Tech Stack: PyTorch, TensorFlow, Keras, OpenCV LangChain, LlamaIndex, FAISS, ChromaDB NumPy, Pandas, Scikit-learn, Matplotlib AWS SageMaker, Rekognition, GCP Vision API Why Clients Choose Me: - Top Rated Plus freelancer (Top 1%) - Proven $5M+ revenue impact - Production-grade AI systems - Clear communication and fast delivery - Business-focused AI solutions Typical Projects: • Custom LLM fine-tuning and private GPT systems • AI chatbots for SaaS and customer support • Computer vision pipelines • AI-powered SaaS platforms • Intelligent data products • AI workflow automation

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

I'm a Senior Computer Vision Engineer with 7+ years of professional experience delivering enterprise-grade Computer Vision, Edge AI, Deep Learning & Machine Learning Solutions. I have a solid foundation in state-of-the-art deep learning models and machine learning algorithms, applying them to build, deploy, and optimize production-ready systems across edge devices, cloud platforms, and agentic AI pipelines. ✅ What I Build ▸ Computer Vision: object detection, tracking, segmentation, pose estimation, image classification, counting, OCR, anomaly detection ▸ Edge AI: NVIDIA Jetson (Nano, Orin, Xavier), Raspberry Pi, model optimization with TensorRT, ONNX, and TFLite for real-time inference ▸ Image Generation: Stable Diffusion (SDXL, SD 3.5), Flux, DALL·E 3, ControlNet, LoRA fine-tuning, ComfyUI workflows ▸ Video Generation: Kling, Runway Gen-3, Minimax Hailuo, Veo, Pika, automated cinematic and product video pipelines ▸ AI Agents: autonomous multi-step agents using LangChain, LangGraph, CrewAI, and AutoGen with RAG, memory, and tool use ▸ Voice AI: conversational voice agents using Vapi, Retell AI, ElevenLabs, Bland AI, and Deepgram for inbound/outbound calling and automation ▸ MCP Servers: custom Model Context Protocol servers connecting Claude and other agents to your APIs, databases, and internal tools ▸ Claude Code: agentic software engineering with subagent architectures, skill-based pipelines, and autonomous multi-step coding workflows ✅ Tech Stack ▪ Computer Vision: OpenCV, YOLO, MediaPipe, Detectron2, SAM, Vision Transformers ▪ Deep Learning: PyTorch, TensorFlow, Keras, ONNX ▪ Tracking & Optimization: DeepSORT, ByteTrack, TensorRT, OpenVINO ▪ Deployment: DeepStream, Triton Inference Server, TFLite, FastAPI, Flask, Docker ▪ Generative AI: Stable Diffusion, Flux, ComfyUI, Replicate, Fal.ai ▪ Agents & Voice: LangChain, LangGraph, CrewAI, Vapi, Retell AI, ElevenLabs, n8n ▪ MCP & Agentic: Claude Code, MCP SDK, custom tool servers ▪ Cloud: AWS, GCP, Azure ▪ Languages: Python, C++, CUDA 🚀 Send me a message with what you are trying to build, and let's discuss.

I am an AI Engineer with 4+ years of experience building and deploying production-ready AI systems across classical machine learning, deep learning, computer vision, NLP, and Generative AI. Unlike many AI developers who focus only on LLMs, I work across the entire AI stack. I believe the best solution isn't always a large language model or an expensive API. Many real-world problems are better solved using classical machine learning or deep learning, resulting in lower infrastructure costs, faster inference, reduced latency, and greater control over your solution. My goal is always to build the most effective system not the most expensive one. Some of the areas I regularly work in include: * Classical Machine Learning (XGBoost, LightGBM, CatBoost, Random Forests, SVMs, feature engineering, predictive modelling, forecasting, anomaly detection, recommendation systems) * Deep Learning (PyTorch, TensorFlow, CNNs, Transformers, Vision Transformers, knowledge distillation, model optimization) * Computer Vision (object detection, image classification, segmentation, OCR, document understanding, face recognition, multi-object tracking, embedding-based search) * NLP & LLMs (RAG, GraphRAG, agentic workflows, fine-tuning, embeddings, semantic search, document QA, information extraction) * Generative AI applications using OpenAI, Anthropic, Gemini, and open-source models * End-to-end AI pipelines from data collection and preprocessing to training, evaluation, deployment, and monitoring I also have extensive experience optimizing AI models for production through knowledge distillation, pruning, quantization, and efficient inference, making models smaller, faster, and more cost-effective for both cloud and edge deployments. On the engineering side, I work comfortably with Python, FastAPI, PostgreSQL, pgvector, asynchronous programming, Docker, GPU acceleration, and cloud deployments. I build complete AI products and APIs that are designed to scale not just research prototypes. Beyond implementation, I enjoy solving difficult research and engineering problems. Whether it's designing a predictive model, improving model accuracy, reducing inference costs, building an intelligent document processing pipeline, or deploying an LLM application, I focus on solutions that are reliable, maintainable, and practical for production. I also lead a team of AI engineers, giving me experience not only in technical execution but also in planning, code quality, mentoring, and delivering projects on time. If you're looking for someone who can understand the problem first, choose the right AI approach, and build a production-ready solution that balances performance, cost, and scalability, I'd be happy to help.