Need a Computer Vision Engineer who can turn image or video data into a reliable production system? I build real-time computer vision and machine learning pipelines for object detection, tracking, segmentation, pose estimation, OCR/ANPR, industrial inspection, and video analytics. My core stack is Python, PyTorch, YOLO, OpenCV, ONNX Runtime, TensorRT, CUDA, FastAPI, and Docker. 3+ years in Computer Vision and ML 100% Upwork Job Success Score with consistent 5-star feedback $20K+ delivered across Upwork and direct engagements Selected results: * Built and evaluated an automotive component-inspection system using 20,000+ images * Developed real-time posture and movement analysis with custom keypoint logic, calibration, temporal smoothing, and CPU-friendly inference * Built multi-camera vehicle detection, persistent tracking, ANPR/OCR, and event logging * Optimized a background-removal pipeline from 2+ seconds to 0.8 seconds and reduced 4K image-saving time from about 13 seconds to 2 seconds I can help with: Computer Vision and Deep Learning * Object detection, classification, instance/semantic segmentation, pose estimation, and tracking * YOLO training, transfer learning, dataset preparation, augmentation, evaluation, and error analysis * RTSP, CCTV, webcam, multi-camera, and recorded-video analytics * OCR, ANPR/ALPR, document vision, barcode, label, and product recognition * Industrial inspection, defect detection, image registration, measurement, and anomaly detection * Research-paper implementation and custom algorithm development Inference Optimization and Deployment * PyTorch to ONNX conversion and ONNX Runtime/TensorRT acceleration * FP16/INT8 quantization, batching, profiling, memory reduction, and CPU/GPU optimization * FastAPI/REST APIs, Docker, Redis workers, databases, dashboards, and Electron integration * Using NVIDIA Triton Inference Server or NVIDIA Deepstream for pipelines and delivery * Deployment on Windows, Linux, AWS, RunPod, local servers, and edge hardware You receive tested, maintainable code, clear documentation, and a system designed around your target accuracy, FPS, latency, privacy, hardware, and operating environment. Send me your sample images/video, current code or model, target hardware, and required accuracy/FPS. I’ll recommend the most practical route from prototype to deployment.

⭐⭐⭐⭐⭐ 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!

My machine-learning work has covered bird detection around high-voltage equipment, industrial sensor anomalies, document images, and demand forecasting. I handle the data, model, evaluation, and deployment as one piece of work. A model can look good in a notebook and still fail once it sees poor lighting, noisy sensors, limited hardware, or changing real-world data. I measure false positives, missed detections, inference time, memory use, operating cost, and data quality before deciding whether the model is ready. For Power Grid Corporation of India Limited, I worked on an edge-AI bird-deterrence system for high-voltage infrastructure. A YOLO and OpenCV pipeline detects birds near transmission equipment, with ONNX Runtime running inference on the deployed device. The system then activates predator sounds and light patterns. Outdoor conditions, limited compute, and false alarms were part of the testing from the beginning. Anomix is an anomaly-detection platform for power plants and similar facilities. It ingests sensor readings, learns normal operating patterns, and flags unusual behavior in about two seconds. I worked on the model pipeline and the application used by plant teams to review anomalies before they turn into equipment failures or safety events. For a restaurant group, I used 279,143 transaction records to forecast hourly order volume, gross sales, and staffing demand across several locations. The work included data preparation, feature design, model comparison, and a dashboard that operations teams could use without reading the modeling code. I also take on: • Object detection, segmentation, and tracking with YOLO or PyTorch • Video analytics and inference on edge devices • OCR and document-image processing • Sensor anomaly detection and predictive maintenance • Demand forecasting and other time-series models • Model APIs, evaluation pipelines, and production deployment I keep datasets, training parameters, model versions, and evaluation results reproducible. When labels are limited, I help define the annotation process and test set before tuning begins. I can also review an existing model, reproduce its failures, and tell you whether the next step is better data, a different architecture, or a deployment change. Main tools: Python, PyTorch, TensorFlow, YOLO, OpenCV, ONNX, scikit-learn, pandas, NumPy, FastAPI, AWS, and Docker.

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 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.

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.

Top Rated Plus ML engineer: medical imaging (segmentation, registration, nnU-Net), clinical NLP, deep computer vision. 100% Job Success · 1,200+ hrs. I take on the problems where the obvious approach fails. WHERE I DO MY BEST WORK Medical imaging ML. I evaluate and debug segmentation models (Dice scores, Bland-Altman analysis), run rigid and deformable image registration (ANTs/SyN), handle nnU-Net preprocessing and training, and deliver 3D Slicer overlays your clinical team can actually inspect. On my longest imaging engagement I sent a demo video at every milestone, so the client never had to guess where things stood. Before Upwork, I worked on chest X-ray segmentation and calcification detection for a US medical-device software company. Clinical & scientific NLP. 600+ hours (and counting) on a research-heavy clinical NLP project: sentence embeddings, UMAP + HDBSCAN clustering, custom composite scoring, synthetic training data. The kind of work where you form a hypothesis, run the experiment, and let the data kill it. (Under NDA) Deep computer vision. I trained a Flux DensePose ControlNet from scratch — dataset prep, captioning, GPU training, parameter tuning — then shipped it behind a Streamlit app so the client's non-technical team could generate images without me. Also: YOLO detection, and an automated color-correction pipeline that detects a color card and generates 3D LUT/CUBE files across EXR, CR2, CR3, and PNG workflows. Document intelligence & LLM pipelines. I build document-processing pipelines that extract structured data from messy PDFs, scans, invoices, and reports — OCR, transformer-based page classification, and LLM extraction with Claude/OpenAI — delivering clean CSV or JSON. I've also built cross-referencing systems that connect information across multiple documents, and raised extraction accuracy on a pipeline other teams had given up tuning. I ALSO GET CALLED WHEN SOMETHING IS SILENTLY BROKEN Two of my favorite projects were rescues: a Temporal Fusion Transformer backtest that was silently leaking data (fixed the encoder/decoder windowing, ran leakage checks, rebuilt walk-forward validation), and a multi-timeframe trading pipeline writing empty tags (root-caused it, patched it, added audit scripts and trace columns). If you need someone to debug and fix an ML model, training pipeline, or backtest that's producing numbers nobody quite trusts, that is a job I know how to do. HOW I WORK Fast replies — usually within hours, working Nepal time with real overlap across US, EU, and AU hours. Milestone demos, often as short videos. Documentation your next engineer can pick up cold. All 13 of my completed contracts are five stars, and several reviews use the phrase "above and beyond", that part I'm proud of. Send me the problem you suspect is too messy for a freelancer. Those are the ones I want.

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.

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.