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I help startups, SaaS companies, and enterprise teams automate workflows, improve knowledge retrieval, and deploy production AI systems that deliver measurable business outcomes. I build production-grade AI agents, multi-agent systems, and RAG platforms that remain reliable in production long after deployment. Verified Upwork track record: 147 jobs delivered, 7,000+ hours, 100% Job Success, Top Rated, 5.0 across 133 reviews. — What I specialize in — Plenty of AI developers can wire up a chatbot. I focus on building AI systems that solve real business problems and hold up in production. • AI agents & multi-agent systems — orchestration, tool calling, LangGraph • Advanced RAG — hybrid search, GraphRAG, reranking, hallucination control, RAGAS evaluation • LLM applications & copilots, including MCP (Model Context Protocol) integrations • Fine-tuning (LoRA / QLoRA / PEFT) and inference optimization • A full ML / CV / NLP foundation underneath, so I solve the problem the way it actually needs solving — not just the way that fits one tool — Recent work — • HirePilot AI — an AI-native talent marketplace (SaaS) running the full hiring pipeline: candidate matching, resume parsing, technical assessments, interview management, and recruiter verification across dedicated candidate, employer, recruiter, and admin portals. The platform processes thousands of candidate profiles and reduces manual screening from hours to minutes. • Enterprise multi-agent GraphRAG platform unifying documents, databases, Jira, and Confluence with hybrid retrieval, graph reasoning, and answer verification. RAGAS-based evaluation improved answer quality by more than 25% while significantly reducing hallucinations across thousands of enterprise documents. • SmartSupportAI — an intelligent customer-support system (n8n, Gemini, Pinecone, Google Drive) that automatically detects new and updated manuals, FAQs, and knowledge-base content, keeping answers aligned with the latest documentation. Reduced document search time by approximately 75% and eliminated a large portion of repetitive support queries. • MCP-based financial research assistant with custom MCP servers integrating market data, news, and technical indicators, enabling multi-agent research workflows and faster investment analysis. • Multi-agent healthcare assistant automating patient workflows including department routing, appointment booking and modification, prescription explanations, patient-history retrieval, and seamless human escalation. • Medical image segmentation for cancer detection using deep learning, achieving strong validation performance, alongside an OCR and computer-vision pipeline for extracting measurements from architectural and construction drawings. Domains delivered in: recruitment, healthcare, finance, e-commerce, IoT, and manufacturing. — Why clients hire me, and rehire me — When you hire me, you work directly with the engineer building the solution. • 147 completed projects backed by a 100% Job Success Score • Production deployments that continue delivering value after handoff • Direct technical ownership from architecture through deployment • Fast iteration with working prototypes delivered early • Honest scoping — I'll tell you when AI isn't the right answer before you spend money on it — Tech — Python, LangGraph / LangChain, n8n, OpenAI / Claude / Gemini, MCP, Pinecone / Weaviate / FAISS, Neo4j, FastAPI, Docker, AWS / Azure / GCP. — Let's talk — If you need an AI agent, MCP integration, production RAG platform, or custom LLM application, send me a message describing the problem you're trying to solve. I'll tell you how I'd approach it, what I'd recommend, and whether I'm the right fit for the project.

Hi, I'm Sabir Ali! I'm a highly skilled Data Annotator/Labeler with over 4 years of experience working at a reputable data labeling company. I have a deep understanding of data labeling processes and a proven track record of success, having completed over 100 data labeling projects. I'm also adept at handling pilot projects and ensuring their smooth execution. I'm proficient in using a variety of data labeling platforms, including Labelbox, V7 Darwin, AWS SageMaker, and Dataloop. My familiarity with these tools allows me to deliver high-quality labeled data efficiently and meet your specific project requirements. I'm confident in my ability to be a valuable asset to your team. Feel free to reach out to discuss your project and how I can contribute! Key Skills: Data Annotation/Labeling Data Labeling Best Practices Pilot Project Management Labelbox V7 Darwin AWS SageMaker Dataloop Benefits of Working with Me: Accuracy and Attention to Detail Experience with Diverse Data Types Efficiency and Speed Area of Expertise: -Data Labeling and Annotation -Image Labeling and Annotation -Video Annotation and Labeling -Audio Annotation and Labeling -Text Labeling and Annotation -Text and Image Classification -Object Detection for Computer Vision -Sentiment Analysis -Quality Control and Assurance -Data Pre-processing -Bounding Box annotation -Polygons Annotation -Semantic Segmentation -3D Cuboid Labeling -Key points Annotation -Tagging with lines and splines -Data Tagging and Classifications

I turn camera feeds into production AI systems, real-time object detection, video analytics, edge deployment. 45+ shipped with my Brainy Neurals team. Most AI camera projects die in the gap between training and shipping. Training a model is the easy 10%. The hard part is compiling for edge hardware, hitting latency budgets, and keeping it running under field conditions, dim lighting, dropped frames, flaky networks. That's the layer I architect personally, with my team handling delivery. Across 45+ production deployments in construction, manufacturing, sports, surveillance, and healthcare, the systems run on NVIDIA Jetson, Qualcomm, and custom low-power silicon. Edge-optimized with TensorRT, orchestrated with DeepStream, monitored in production. Backed by NVIDIA Inception Partner, AWS Activate, and Microsoft for Startups, the three partnerships that tend to matter when enterprise procurement is reviewing the architecture. When a project needs more than cameras, document intelligence, RAG, LLM applications woven into a vision workflow, the Brainy Neurals team ships those too. Typically as extensions of the primary system, not as standalone contracts. How I engage: discovery call to pressure-test feasibility, scoped architecture document before any code is written, then delivery with my team executing and me owning every architecture decision. Projects move fastest when I can see the cameras, the environment, and one sample of the data in the first call. The single thing that separates me from most CV profiles you'll see on Upwork: I've shipped 45 production systems that are still running, which means I know what actually breaks in the field versus what only breaks in a notebook. That's the experience I bring to every architecture call. If you're building a camera-AI system that has to work in a real environment, and you want an architect who stays on the project through delivery, send me a message. Describe your use case, the environment it runs in, and your rough timeline. I respond within 24 hours with an honest read on whether I can help, how I'd approach it, and what a realistic delivery timeline looks like.

Custom-trained AI vision systems that detect manufacturing defects, count warehouse stock, protect retail floors from theft, and inspect garments for fabric flaws built for industrial buyers who need a vision model that actually works on their data, not a generic demo. I work with the Brainy Neurals team as the model-builder for industry-applied computer vision. My focus is the layer between "we have cameras" and "we have actionable detection" collecting the dataset, annotating it, training the model, evaluating it against real production conditions, shipping it as a working system. WHAT I BUILD Defect detection and quality inspection surface scratches, weld defects, packaging flaws, print errors, missing components, assembly mis-orientations, fabric defects, garment inspection. Custom-trained YOLO and segmentation models on your production images, evaluated on the edge cases that matter for your defect distribution. Retail analytics and loss prevention footfall counting, dwell-time heatmaps, queue analytics, planogram compliance, shelf-out detection, theft and shoplifting detection, restricted-zone intrusion, customer journey mapping. Runs on existing store cameras. Warehouse and logistics vision pallet counting, dock monitoring, SKU recognition, package counting on conveyors, forklift safety zones, PPE compliance (helmet, vest, gloves, harness), worker fall detection. Reduces shrinkage, audit exposure, safety incidents. Textile and apparel inspection fabric defect detection across woven, knit, and non-woven materials. Holes, stains, weave faults, print misregistration, colour variation, missing stitches, garment QC at finishing. Sports analytics player tracking with persistent IDs, ball detection, action recognition (shots, passes, fouls, rebounds), zone-aware scoring, jersey-digit recognition, highlight clips. Healthcare and patient safety fall detection in wards and assisted living, gait analysis, hand hygiene compliance, bed occupancy, restricted-area monitoring. Pose-based detection without face recognition where regulation requires. THE STACK I USE YOLOv11 and YOLOv12 when throughput matters; RT-DETR and Mask R-CNN when precision matters more than speed; SAM and Grounding DINO when datasets are small and zero-shot bootstrapping helps. Tracking is ByteTrack or DeepSORT depending on occlusion. Pose uses MediaPipe and custom keypoints. Foundation: PyTorch, OpenCV, ultralytics, timm, albumentations. Annotation and dataset work is where most CV projects quietly fail. I work with Roboflow, Label Studio, CVAT, and build custom tooling when off-the-shelf options corrupt formats. Datasets version-controlled. Edge cases sampled deliberately. Class imbalance handled with focal loss, augmentation, or class-aware sampling. Evaluation goes beyond mAP. I report precision, recall, F1, confusion matrices on rare classes, and false-positive rates at the operating threshold you care about. If your cost of a false positive differs from a false negative, the model is tuned for that asymmetry. WHO I BUILD FOR Manufacturing automotive, electronics, food and beverage, pharma packaging (blister packs, labels). Industry 4.0 quality, MES, OEE teams. Warehouses and 3PLs pallet count automation, dock-to-stock, shrinkage reduction, returns inspection. Retail fashion, grocery, big-box, convenience. Loss prevention, store ops, planogram, customer-experience teams. Textile and apparel fabric mills, garment factories, finishing units, brand QA. Sports broadcast, performance analytics, training instrumentation. Construction and AEC progress detection, structural element counting, site safety, drone inspection. Healthcare patient fall prevention, ward safety, hand hygiene, infection-control compliance. HOW I WORK Projects start with a 30-minute call where I look at sample images or video from your environment. I tell you whether a model can do what you want, dataset size needed, realistic accuracy ceiling, and where the model will fail. I would rather decline than over-promise on a dataset that cannot support it. Pricing is fixed-scope per milestone annotation, baseline training, evaluation, retraining, integration handoff. Hourly only for maintenance after shipping. THE BRAINY NEURALS BACKING I work with the Brainy Neurals team 15 AI engineers, NVIDIA Inception Partner, AWS Activate, Microsoft for Startups. When a project needs camera infrastructure, edge deployment, or multi-camera coordination, that capacity sits with the team I bring them in cleanly, you do not manage two vendors. For pure model-building I lead end to end myself. LET'S TALK IF You have images or video from your environment, you know what you want detected, and need a model trained on YOUR data not a generic detector that misses 30% of edge cases. Send a sample with your first message. I reply within 24 hours with a feasibility read, dataset size, timeline.

✔️ TOP RATED Freelancer specializing in Computer Vision and AI-based image processing systems. I enjoy building practical, production-ready solutions for real-world visual problems and would be glad to contribute to your project in a meaningful way. My core focus is on computer vision, especially tasks involving image understanding, detection, and extraction from complex or noisy inputs. I have strong experience working with: ✔️Image Classification & Fine-Grained Recognition (handling subtle visual differences) ✔️Object Detection (YOLO, SSD, Faster R-CNN, TFOD API) ✔️Image Segmentation (Mask R-CNN, semantic & instance segmentation) ✔️OCR & Text Extraction (structured documents, multi-format, noisy images) ✔️Image Preprocessing (denoising, deskewing, perspective correction, enhancement) ✔️OpenCV-based pipelines for real-time and production use ✔️Deep Learning frameworks: TensorFlow, Keras, PyTorch ✔️CNN Architectures: ResNet, VGG, Inception, EfficientNet ✔️Transfer Learning & Custom Model Training ✔️Synthetic Data Generation & Augmentation ✔️Vector Embeddings & Image Similarity Systems ✔️End-to-End CV Pipelines (data collection → training → deployment) Alongside this, I also have a solid foundation in: ✔️Machine Learning & Deep Learning ✔️Mathematics & Statistics (for model understanding and optimization) ✔️Python ecosystem (NumPy, Pandas, SciPy, etc.) ✔️API Development & Deployment(Docker, AWS, GCP) I hold a Bachelor’s degree in Computer Engineering and am currently pursuing a Master’s in AI, which helps me stay aligned with the latest advancements in the field. My approach is always to first understand the business problem and real-world constraints, and then design a solution that is accurate, scalable, and practical to use. I care deeply about delivering solutions that actually work for clients not just in theory, but in real-world conditions. Thanks & Regards, Ruchir

High-quality AI training data can make or break your model. I help AI/ML teams scale data annotation with 95–99% accuracy, backed by structured QA workflows and reliable turnaround, across image, text, and audio datasets. If you're building computer vision or NLP models, I ensure your data is clean, consistent, and production-ready. 1. Experience handling large-scale datasets (10K to 1M+ annotations) 2. Multi-level QA pipelines (review, sampling, audits) 3. Expertise across CV, NLP, Geospatial, and ADAS use cases With 5+ years of experience in AI training data, I’ve consistently delivered high-accuracy datasets with fast turnaround and scalable workflows. I also provide access to multilingual annotation teams (German, Spanish, French, Portuguese, Japanese, etc.), enabling global dataset support. What I can help you with: • Image Annotation (Bounding Boxes, Segmentation, Tagging) • Text Annotation (Classification, Sentiment Analysis, NLP Tasks) • Audio Annotation (Event Labeling, Timestamping) • Data Validation & Quality Assurance I operate with a trained and scalable team of annotators, allowing me to handle both small pilot tasks and large-volume projects efficiently. Why clients choose to work with me: • 95–99% accuracy with strong QA systems • Structured workflows and clear communication • Ability to scale quickly based on project needs • On-time delivery with consistent quality I’m available to start immediately and open to both pilot tasks and long-term collaborations. I’m happy to start with a small sample or pilot task to demonstrate quality before scaling.