Senior Computer Vision / AI Engineer — Paid Proof-of-Concept Project

Senior Computer Vision / AI Engineer — Proof-of-Concept Development AiChairWatch™ is seeking an expert-level Computer Vision / AI Engineer to develop a focused proof of concept for a patent-pending recreational seating-management technology. This is a paid, project-based independent contractor engagement. The initial objective is to establish technical feasibility and build a controlled POC—not a full commercial SaaS platform. Project Scope The POC will use computer vision and video analytics to detect people and recreational seating, establish and maintain temporary person-to-seat associations, track movement within defined spatial areas, generate state-based events, and support a lightweight operational dashboard. The engineer will be expected to help determine the appropriate technical architecture rather than simply implement a predetermined technology stack. Initial work will include: Review confidential technical requirements under NDA Evaluate technical feasibility and recommend the POC architecture Process live and/or recorded camera video Detect people and recreational seating Establish temporary person-to-seat associations Maintain associations as people move through the monitored environment Handle temporary occlusion and tracking loss Determine movement relative to configurable spatial zones Generate defined system events and state transitions Implement basic event/timing logic Create a lightweight staff-facing POC dashboard Log system events and test results Measure and document performance, limitations, and failure conditions Provide recommendations for subsequent pilot development Required Experience We are specifically looking for someone with substantial hands-on experience in real-world computer vision/video analytics. Strong experience should include several of the following: Python OpenCV YOLO or comparable object-detection frameworks Multi-object tracking Person tracking and/or re-identification Occlusion handling Spatial reasoning / regions of interest / geofencing Video-stream processing RTSP/IP camera integration Event-driven application development REST APIs/backend development Git-based source control Experience with technologies such as ByteTrack, BoT-SORT, DeepSORT, NVIDIA DeepStream, Jetson, ONNX, TensorRT, or comparable CV/edge-AI technologies is desirable but not mandatory. What We Are NOT Looking For This is not primarily a generative-AI, ChatGPT, LLM, chatbot, or prompt-engineering project. The primary technical challenge involves computer vision, persistent object/person tracking, spatial reasoning, and reliable state determination from video. Initial Development Approach The engagement is expected to begin with a paid technical feasibility and architecture milestone. The selected engineer will review the detailed requirements, evaluate representative video/test conditions, identify technical risks, recommend the detection/tracking architecture, and define measurable POC acceptance criteria. Upon successful completion of that milestone, the engagement may proceed into POC development. The initial POC will intentionally use a small controlled environment rather than attempt to build the complete commercial platform. Intellectual Property & Confidentiality AiChairWatch™ involves patent-pending technology. Detailed technical specifications and proprietary operating logic will be provided only to selected candidates after execution of a confidentiality agreement. Development work will be governed by written provisions addressing confidentiality, source code, work product, intellectual property, third-party/open-source components, and ownership/assignment of applicable development results. When Applying Please answer the following: Describe the most relevant computer-vision system you have personally designed or implemented. What experience do you have with multi-object/person tracking? How have you handled temporary occlusion, lost tracks, and re-identification? Have you processed live IP-camera/RTSP streams in a production or prototype environment? Please describe. Consider this simplified scenario: A camera observes several recreational chairs. A person occupies one chair, gets up, walks through the monitored area, is temporarily obscured by other people, and later leaves the area. At a high level, how would you maintain the person's association with the original chair and determine when that person has actually left the monitored area? What would you want to evaluate before selecting the computer-vision architecture for this POC? Please provide links to relevant GitHub repositories, demonstrations, publications, portfolio examples, or other technical work where available. Clearly identify which portions of the examples you personally designed or implemented. What is your availability during the next 60 days? Please provide your hourly rate and/or preferred structure for an initial paid technical-feasibility milestone. U.S.-based candidates preferred. AiChairWatch™ AI-Powered Recreational Seating Management Patent Pending