I build production-ready LLM systems and agentic AI pipelines that automate complex workflows. Top Rated on Upwork | 20+ projects in multi-agent AI, generative models & data engineering with PyTorch. Clients come to me when they need AI systems that actually ship — not prototypes. Whether it's a multi-agent orchestration layer, a custom RAG pipeline, a diffusion model for audio/image generation, or a data engineering workflow at scale, I handle the full cycle from architecture to deployment. What I work on: → Agentic AI & Multi-Agent Systems (LangChain, LangGraph, Groq, tool-use agents) → LLM Integration & RAG (retrieval pipelines, fine-tuning, prompt engineering) → Generative AI — Audio & Image (diffusion models, voice synthesis, latent space modeling) → ML Model Development (PyTorch, TensorFlow, classification, NLP, CV) → Data Engineering & Pipelines (PySpark, AWS SageMaker, ETL, 100GB+ datasets) Recent work includes a 7-layer multi-agent data processing engine, a latent diffusion model for synthetic medical audio generation (reduced FAD from 18.82 → 14.71), and a natural language chatbot interface for Grafana dashboards. I communicate clearly, deliver on time, and document everything. I don't disappear mid-project and I don't overpromise scope. If something's not feasible, I'll tell you upfront. If you're building something serious in AI — let's talk. Send me a message and I'll usually reply within a few hours.

𝐈 𝐛𝐮𝐢𝐥𝐝 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐬𝐡𝐢𝐩𝐬. Founder-engineer vibes, sleeves rolled up, results on the board. I turn messy real-world video into stable, low-latency systems your team can trust, and your CFO can love. 𝐍𝐨𝐭𝐜𝐡𝐚 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 𝐆𝐮𝐲 😎 I don’t stop at a cool demo. Shipping LootMart (hyper-local marketplace) taught me the full stack around models: clean APIs, rock-solid data contracts, observability, security, and predictable costs. That’s why my CV/ML services behave like products, not science projects. 𝐖𝐡𝐚𝐭 𝐈 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐃𝐨 - Computer Vision & Video Analytics (2D/3D): detection (YOLO/DETR), multi-object tracking (ByteTrack/DeepSORT), segmentation (U-Net), OCR/document AI, pose/re-ID, visual search & face/product matching (Siamese + Triplet Loss), point clouds & geometry. - High-Throughput Inference: NVIDIA Triton (dynamic batching, concurrent models, HTTP/gRPC), TensorRT (FP16), ONNX Runtime; autoscaling containers with health checks and graceful rollouts. - Robust Ingestion: multi-RTSP pipelines with back-pressure control using OpenCV, FFmpeg, PyAV/decord so frames don’t mysteriously vanish under load. - MLOps & Services: FastAPI/Flask gateways, worker queues, CI/CD, Docker + Nginx; W&B for experiments; versioned datasets; reproducible training. - Data & Integrations: Postgres (schema design, RLS, SQL/PLpgSQL), Redis, vector DBs (Milvus/Qdrant), webhook-driven architectures, n8n workflows for ETL/alerts, and MCP (Model Context Protocol) to wire AI tools into your internal systems. - Selective Full-Stack Glue (when it helps): Next.js app layers, secure webhooks, auth, real-time updates, and crisp dashboards so stakeholders can see impact. 𝐏𝐫𝐨𝐨𝐟 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐮𝐝𝐝𝐢𝐧𝐠 (𝐑𝐞𝐜𝐞𝐧𝐭 𝐖𝐢𝐧𝐬) 1. Triton-backed real-time CCTV analytics across multiple cameras on commodity GPUs (dynamic batching = buttery latency). 2. Visual matching pipelines (Siamese/Triplet) for search/dedupe with rigorous evals and W&B tracking. 3. Heavy research models → ONNX/TensorRT → low-latency services that actually survive production traffic. 4. Production plumbing that lasts: Postgres-first data contracts, webhook fan-out, n8n automations... no brittle glue. 𝐇𝐨𝐰 𝐖𝐞’𝐥𝐥 𝐖𝐨𝐫𝐤 (𝐑𝐎𝐈 𝐅𝐢𝐫𝐬𝐭, 𝐀𝐥𝐰𝐚𝐲𝐬) 1. 30-min discovery → lock in the KPI (latency, accuracy, throughput, cost). 2. Roadmap & estimate → phases, risks, acceptance tests. 3. Build & validate → baselines first, then iterate; measurable deltas each milestone. 4. Handoff & scale → docs, runbooks, and knowledge transfer so your team owns it. 𝐂𝐨𝐫𝐞 𝐒𝐭𝐚𝐜𝐤 Python • PyTorch • TensorRT • ONNX Runtime • NVIDIA Triton • OpenCV • Kornia • FFmpeg • PyAV/decord • Postgres • Redis • Milvus/Qdrant • FastAPI/Flask • Next.js • Docker • Nginx • Weights & Biases • Webhooks • n8n • MCP 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 Consulting/part-time (fractional) engagements: architecture reviews, performance tuning, prototypes, or owning a CV/ML workstream. Top-rated on Upwork. Minimum $100/hr. If you want production-ready computer vision, real-time video, reliable pipelines, and clear ROI, 𝐥𝐞𝐭’𝐬 𝐭𝐚𝐥𝐤. I’ll map your goal to a pragmatic plan and ship results you can measure.

Hi, I’m Rizwan 👋 I’m an AI and Computer Vision Engineer with a strong background in deep learning, YOLO models (v5–v13), PyTorch, and TensorFlow. Over the years, I’ve worked on projects ranging from object detection and tracking to pose estimation, segmentation, and real-time video analytics. I enjoy building solutions that don’t just run in research notebooks but actually work in production, whether that’s on the cloud, edge devices, or mobile applications. My expertise includes: Object Detection & Tracking – YOLO, Faster R-CNN, Mask R-CNN, RetinaNet Segmentation & Classification – SAM, FastSAM, MobileSAM, U-Net, EfficientNet Pose Estimation & Action Recognition – fitness monitoring, sports analysis, human activity recognition. MLOps & Deployment – ONNX, TensorRT, OpenVINO, Docker, Flask/FastAPI, AWS, GCP, RunPod Multimodal AI – combining vision + language with CLIP and Florence-2 I’ve also contributed to Ultralytics (YOLO), where I worked on solutions and open-source contributions used by thousands of developers worldwide. Alongside development, I also create technical tutorials and documentation to make AI projects easier for others to understand and use. If you’re looking for someone who can take your computer vision idea from dataset to deployment, I’d be happy to help. Let’s connect and discuss how we can bring your project to life 🚀

I'm an experienced researcher in machine learning, artificial intelligence, signal processing, and designing decoders for 5G/6G. I can help you with designing and implementing machine learning algorithms for a wide range of applications (e.g., finance, biology, and energy consumption). I actively contribute to the open source projects. Knows Python, C++, CUDA C++, MATLAB, Linux, TensorFlow, PyTorch, PyQt5, Polars, LightGBM, CatBoost, and XGBoost. I also have experience working with Verilog and FPGAs.

ML Engineer — Computer Vision, Claude/LLM Agents & Audio AI I build deep learning systems across vision, language, and audio — and ship them to production, not just notebooks. I've trained models from scratch, fine-tuned transformer architectures, and deployed to Core ML on real client engagements (including a 400+ hour model-build contract). 5.0 stars across every reviewed project. Computer Vision Image classification, detection, and feature extraction with CNN and transformer (ViT/AST-family) architectures. My core audio work runs on spectrogram based vision models, so the same deep-learning toolkit — convolutions, attention, transfer learning — carries directly into image and video tasks. Claude / LLM Agents & Automation AI agents, automation workflows, and reasoning assistants built on Claude and OpenAI APIs. RAG pipelines, tool-use/agentic systems, and LLM integrations that connect models to real data and real actions. Audio & Speech AI Instrument/audio classification, feature extraction, and transformer fine-tuning (AST, Wav2Vec2, HuBERT), plus generative audio with AudioCraft/MusicGen. Built multiple audio models end to end — clients describe me as central to their ML work. Deployment & Engineering TensorFlow/PyTorch → Core ML export, FastAPI inference services, and optimized real-time / batch pipelines. I handle the messy export and optimization steps end to end. Top Rated · 100% Job Success · 5.0 across all reviewed jobs. I move fast and build things that actually deploy.

⭐⭐⭐⭐⭐ AI/ML Engineer | 100% Job Success | Computer Vision, Recommendation Systems, AI-powered Insights, Chatbots, LLMs and RAG Systems Hi! I'm Ali Raza, an AI/ML Engineer with deep expertise in Computer Vision and Large Language Models. I build and deliver production-grade AI systems that drive real business results—from custom vision models to intelligent RAG pipelines ready for deployment. My focus is delivering fast, scalable, and reliable AI systems that integrate seamlessly into real-world products. 🏅 Expert in Tensorflow, PyTorch, LangChain and Production-Ready AI Solutions ⭐ 100% Job Success | Building Scalable Computer Vision Systems and Chatbots for Startups and Businesses 🏆 Specialized in Computer Vision, LLM Applications, and RAG Pipelines ✔ AI/ML Engineer specializing in Computer Vision ✔ LLM and RAG Systems Architect ✔ Python Backend and AI Integration Expert 👁️ Computer Vision Engineering and Development ___________________________________________________________________________ ✔ Object Detection, Segmentation, and Classification ✔ Image-to-Image Systems | Image and Video Processing ✔ Image Recognition, Image Matching, and Colour Matching Systems ✔ Video Analytics and Real-Time Tracking ✔ OCR and Document Processing Pipelines using Tesseract, OpenCV ✔ Custom PyTorch and OpenCV Model Development ✔ High-Performance Inference and Deployment ⚙️ LLMs and RAG Systems ___________________________________________________________________________ ✔ ChatGPT-like Applications and Custom AI Assistants ✔ Retrieval-Augmented Generation (RAG) Pipelines ✔ Vector Database Integration (PgVector, FAISS, Pinecone) ✔ LangChain-based LLM Workflows and Orchestration ✔ Prompt Engineering and Fine-Tuning ✔ Private Document Q&A and Knowledge Systems 🔨 AI-Powered Backend Systems ___________________________________________________________________________ ✔ FastAPI and Python AI Microservices ✔ REST API Development and Integration ✔ Real-time Inference Pipelines ✔ Scalable Backend Architecture for AI Products 📊 Data Engineering for AI Systems ___________________________________________________________________________ ✔ Data Cleaning, Preprocessing, and Feature Engineering ✔ ETL Pipeline Development and Automation ✔ Data Ingestion and Transformation Pipelines ✔ SQL, PostgreSQL, and MongoDB Database Design ✔ Web Scraping and Large-Scale Document Parsing using Playwright, BeautifulSoup 📩 MLOps and Deployment ___________________________________________________________________________ ✔ Docker Containerization ✔ AWS and GCP Deployment ✔ Model Optimization using ONNX and CUDA ✔ Production Deployment and Scaling ✔ CI/CD and Automation for ML Pipelines 🏆 Key Computer Vision Achievements ___________________________________________________________________________ ✨ Built custom object detection models using YOLO, PyTorch, and OpenCV ✨ Fine-tuned vision models using LoRA for efficient domain adaptation ✨ Developed image similarity and matching systems for visual search ✨ Implemented OCR pipelines for automated invoice and document processing ✨ Designed and deployed real-time video processing systems ✨ Containerized and deployed vision APIs on AWS and GCP infrastructure ✨ Optimized models using ONNX, TensorRT, and CUDA for faster inference ✨ Created automated data extraction pipelines for document-intensive workflows 🛠️ TECH STACK ___________________________________________________________________________ ➡️ Core Languages: Python, SQL, C/C++, JavaScript ➡️ Machine Learning Models and Architectures: ML Models: Logistic Regression, Decision Trees, SVM, KNN, Ensemble Methods, PCA, Naive Bayes Deep Learning: CNNs, RNNs, Autoencoders, GANs, Transformers, Large Language Models (LLMs), UNet, YOLO ➡️ Machine Learning and AI: PyTorch, TensorFlow, Scikit-learn, OpenCV, Keras, ONNX Runtime, Sentence-Transformers, spaCy, NLTK, Tesseract OCR, NumPy, XGBoost, LSTM, Transformer Models ➡️ Model Fine-Tuning and LLM Adaptation: LoRA, QLoRA, Transfer Learning, Fine-Tuning LLMs (GPT, LLaMA, Mistral), Instruction Tuning, Supervised Fine-Tuning (SFT), Adapter Layers ➡️ LLM and RAG Frameworks: LangChain, FastAPI, PgVector, FAISS, Pinecone, Vector Databases, Embedding Pipelines ➡️ Backend and APIs: FastAPI, Flask, REST APIs, Microservices Architecture, Streamlit, Uvicorn ➡️ Data Engineering and Databases: PostgreSQL, MongoDB, MS SQL Server, Neo4j, PgVector, Pandas, NumPy, ETL Pipelines ➡️ Cloud and DevOps: AWS, GCP, Docker, CI/CD Pipelines, Model Serving, Scalable Deployment ➡️ Automation and Scraping: Playwright, BeautifulSoup, Automated Data Extraction, Document Parsing 🤝 Let's Work Together! ___________________________________________________________________________ If you're building an AI product, integrating LLMs, or developing Computer Vision systems, feel free to invite me to discuss your project!

I build 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐯𝐢𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 that turn 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐢𝐦𝐚𝐠𝐞𝐬 𝐚𝐧𝐝 𝐯𝐢𝐝𝐞𝐨 𝐢𝐧𝐭𝐨 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞, 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐝𝐚𝐭𝐚 that your product can actually use. I design the 𝐟𝐮𝐥𝐥 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 from 𝐝𝐞𝐞𝐩-𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬 to 𝐀𝐖𝐒 & 𝐆𝐏𝐔 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 (𝐑𝐮𝐧𝐏𝐨𝐝), 𝐃𝐨𝐜𝐤𝐞𝐫𝐢𝐳𝐞𝐝 𝐀𝐏𝐈𝐬, databases, and dashboards so your AI runs 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐲 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. 🧠 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 • 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠: Real-time object detection, multi-object tracking, people & product recognition, part and asset tracking, counting, dwell-time analysis, motion & trajectory tracking, video analytics, cloud-based vision pipelines, YOLO-based detectors, re-identification, and scalable AI vision systems. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Retail & footfall analytics, inventory/warehouse automation, manufacturing & quality control, safety & surveillance, traffic & smart cities, parking systems, healthcare monitoring, robotics & autonomous systems, sports & player tracking, logistics, document/page scanning, smart camera applications. • 𝐕𝐢𝐝𝐞𝐨 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠: AI-powered video analysis, event detection, start/end timestamping, scene segmentation, activity recognition, temporal modeling, automated video tagging, highlight detection, content indexing, multi-modal AI (vision + audio + text), video embeddings, structured timeline generation. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Sports highlights, surveillance & security, training & compliance videos, medical/industrial footage, content moderation, media production, marketing analytics, user behavior analysis, long-form video summarization. • 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Instance & semantic segmentation, pixel-level masks, defect & crack segmentation, document & layout segmentation, background removal, medical/industrial segmentation, deep-learning mask models, high-precision region extraction. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Manufacturing QC, medical imaging, satellite/aerial analysis, document scanning, image editing, autonomous systems, construction inspection, CV-driven automation. • 𝐎𝐂𝐑 / 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐀𝐈: OCR, handwriting recognition, table & form extraction, document classification, layout analysis, key-value extraction, invoice/receipt processing, PDF understanding, LLM-powered parsing into structured data. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Finance/accounting, healthcare records, legal documents, insurance, logistics paperwork, KYC & onboarding, enterprise search, document automation workflows. • 𝐃𝐞𝐩𝐭𝐡 / 𝟑𝐃 𝐕𝐢𝐬𝐢𝐨𝐧: Depth estimation, stereo vision, OAK-D integration, point-cloud processing, 3D object detection, 3D reconstruction, spatial mapping, pose estimation, multi-view geometry pipelines. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Robotics/autonomy, AR/VR, industrial inspection, warehouse automation, medical imaging, smart manufacturing, spatial AI. • 𝐂𝐥𝐚𝐬𝐬𝐢𝐜𝐚𝐥 / 𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐢𝐜 𝐂𝐕: Camera calibration, lens distortion correction, homography & perspective transforms, image alignment, feature matching, visual measurement, motion tracking, optical flow, stabilization, geometric scene understanding. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Document scanning, photogrammetry, robotics navigation, industrial metrology, sports analytics, drone imaging, medical imaging, AR systems. ☁️ 𝐀𝐈 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 & 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 Production-grade AI backends on 𝐀𝐖𝐒, 𝐀𝐳𝐮𝐫𝐞, 𝐚𝐧𝐝 𝐆𝐂𝐏, GPU-accelerated inference on 𝐑𝐮𝐧𝐏𝐨𝐝, Dockerized model services, 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐚𝐧𝐝 𝐛𝐚𝐭𝐜𝐡 𝐀𝐏𝐈𝐬, secure cloud storage pipelines, access-controlled services, logging, monitoring, and fully reproducible deployment environments. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Scalable computer vision platforms, video & image processing pipelines, OCR & document workflows, AI-powered APIs, large-scale data processing, and customer-facing AI products. 🧩 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐀𝐈 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 Backend APIs, database-backed AI workflows, webhook integrations, job orchestration, result dashboards, user-facing tools, authentication when needed, and end-to-end pipelines that connect AI outputs to real products. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Customer-facing AI apps, internal tools, admin panels, SaaS platforms, analytics dashboards, workflow automation, and enterprise system integrations. 💬 𝐋𝐞𝐭’𝐬 𝐜𝐨𝐧𝐧𝐞𝐜𝐭! Send me a message and let’s talk through your requirements.

With having 4+ years of experience in software development to create high performance AI applications that can easily grow from MVP to a scalable product, handle real user traffic and help businesses grow. I specialize in building end-to-end Computer Vision and LLM systems, with strong expertise in backend engineering and advanced skills in Computer Vision, Image Processing, Object Detection and Tracking, YOLO, MLOps, AI Agent Development, fine tuning large language models, and building Retrieval Augmented Generation systems using Generative AI and Artificial Intelligence for any AI App Development. Core Expertise: - Computer Vision : Real-time video analytics, object detection, segmentation, and edge AI systems. Deployment on edge devices and cloud with optimized inference pipelines for low latency and high throughput. I can help you with: - Object Detection: YOLO, Faster R-CNN, SSD - Image Segmentation: U-Net, Mask R-CNN, Segment Anything Model (SAM) - Real-Time Video Analysis: Tracking, anomaly detection, live processing - Medical Imaging: Tumor detection, organ segmentation - Image Classification and recognition systems - Defect Detection in industrial applications - Pose Estimation and keypoint detection - Action Recognition from video streams - Deep learning model training, fine-tuning, and optimization - Model deployment, monitoring, and performance evaluation - Sports analytics - LLM Agent Development : Voice AI Agents, Voice Cloning, LLM Applications, LLM Agents for workflow automation, AI chatbots, AI Model Development, AI Model Integration, AI Speech-to-text and AI Text-to-speech. I can help you with: - Autonomous AI Agents & Workflows: Designing multi-agent systems and stateful workflows using frameworks like LangGraph to automate complex, multi-step business logic. - Advanced RAG Pipelines: Building robust Retrieval-Augmented Generation systems with advanced chunking strategies, hybrid search, re-ranking, and vector databases to eliminate hallucinations. - Real-Time Voice AI & Avatars: Integrating high-fidelity speech-to-text, low-latency LLM processing, custom voice cloning (TTS), and lipsync synchronization for interactive AI avatars and coaches. - Model Fine-Tuning & Optimization: Quantizing, fine-tuning, and adapting open-source models (Llama, Mistral) for domain-specific tasks while maintaining low serving costs. - High-Performance LLM Serving: Setting up multi-GPU, high-throughput inference backends utilizing vLLM, Nvidia NIM, andoptimized KV cache management. - Function Calling & Tool Use: Empowering LLMs to securely interact with third-party APIs, relational databases, legacy systems, and external analytical tools. Tools and Tech: Python | OpenCV | PyTorch | Scikit-Image | Albumentations | Computer Vision | Video Analytics | Object Detection | OCR | Ollama | vLLM | OpenAI | Huggingface | Transformers | Vector Database | Milvus | Pinecone | Chroma | Redis Nvidia DeepStream | TensorRT | Jetson Orin | Nvidia Triton Inference Server | Edge Deployment | Video Analytics Pipeline AWS | GCP | Azure | Cloud Deployment | Scalable Cloud Architecture Docker | Kubernetes | Microservices | MLOps | CI/CD Security Best Practices | API Protection | System Design | High-Performance Architectures What I Deliver: Production-ready AI MVPs that scale Full stack Computer Vision and LLM applications High-performance backend systems and APIs Real-time computer vision pipelines Scalable cloud deployments with monitoring and logging Clean, maintainable, and well-documented code If you're looking to build a high-performance Computer Vision or AI Agent system or scale your existing MVP, I can help you validate your idea, design system architecture, build your MVP, and scale it into a production-ready Computer Vision product with strong performance, reliability, and business impact. Keywords: Computer Vision, Object Detection and Tracking, YOLO, OpenCV, Real-Time Video Analytics, Nvidia DeepStream, Image Processing, Image Analysis, OCR, Edge AI, Computer Vision Software, Nvidia Triton Inference Server, Multi-GPU Inference, PyTorch, TensorFlow, Deep Learning, Neural Network, Machine Learning, MLOps, AI Deployment, Docker, AWS, Azure, Python, FastAPI, Django, Segmentation, Image Classification, Face Recognition, Pose Estimation, Action Recognition, Optical Flow, Depth Estimation, Image Generation, Anomaly Detection, Vision Transformer, CLIP, SAM, MediaPipe, Data Augmentation, Transfer Learning, Fine-Tuning, Model Quantization, Camera Calibration, Stereo Vision, LiDAR, Point Cloud Processing, Autonomous Driving, Medical Image Analysis, Kubernetes, AWS SageMaker, Google Cloud Vertex AI, ONNX, Flask, Streamlit, Jetson Nano, Embedded Vision, Pattern Recognition, Biometrics, Video Surveillance, Defect Detection, Industrial Automation, Robotics, Face Detection, Pedestrian Detection, Lane Detection, Autonomous Vehicles, Medical Imaging, CNN, Transformers, ChatGPT, Natural Language Processing

Experienced data scientist since 2018. Have worked in a leading geospatial-focused company, developing machine learning, computer vision, data science, and GIS solutions. Why Work with Me? ✅I welcome proof-of-concept development, validate your solution's feasibility, no string attached! ✅I focus on delivering truly tailored solutions that solve your specific problems. ✅I bring a proven track record of successfully delivering projects that meet client goals (check my portfolio!) Don't hesitate to reach out to discuss your next solution! Core Skills: GIS & Remote Sensing: - Data analysis and automation using Python (GDAL, Rasterio, Geopandas) - Remote sensing workflows with Sentinel, Landsat, UAV imagery, and more - Proficient with QGIS, ArcGIS, Google Earth Engine Geospatial Data Science: - AI-powered Image classification, Object detection, Semantic segmentation, and Instance Segmentation (PyTorch, YOLO) - Machine learning for geospatial data (Scikit-learn, OpenCV, Scikit-image, XGBoost) - Data annotation in Roboflow, Label Studio, CVAT, and many more Web Development: - Backend development with Django & Flask, and FastAPI - Postgres/PostGIS database implementation. - JS, HTML, CSS

AI Expert | Machine Learning • Deep Learning • Generative AI I'm a senior AI Engineer with 4+ years of hands-on experience in machine learning and deep learning and has a strong grasp in both research and development project I can help you to build the whole ML pipeline from scratch or improve your solutions. Dealing with various tasks, such as Machine learning, NLP, Time Series Analysis, Computer Vision, Medical EHR and imaging Data, GANs, RAG, Chatbot, Image generation and developing bots for Stocks or/and Crypto markets, deploying APIs for ML models, etc. Skills: - Languages: Python - Frameworks: PyTorch, TensorFlow, Keras, Flask - Libraries: LangChain, NumPy, Pandas, Scikit-Learn, Matplotlib, Plotly, SciPy, NLTK, LightGBM, XGBoost, langchain, pytorch - DB: MySQL, PostgreSQL, MongoDB, VectorDB - Version control: GIT - Other: Docker, Google Cloud, LLM, ChatGPT, LLaMA, OpenAI, Grok, qwen Feel free to reach out I can add the AI touch and technical backbone to your next big idea.