I’m assembling a small, highly-skilled development squad to finish a platform that sees the world, talks to external services that never published their docs, and then deploys itself at scale. Python sits at the heart of the codebase, so deep confidence with modern Python, virtual environments and dependency management is essential. Most of the heavy lifting in computer vision happens through OpenCV and complementary deep-learning libraries; the pipeline must handle object detection, image processing and facial recognition with equal reliability and speed. If you also think in C++ or Java when performance demands it, that flexibility will be welcome. Beyond vision, we have to collect data from third-party applications that do not provide friendly endpoints. That means reverse-engineering network traffic, performing API data extraction, applying selective software modification, and validating everything through security-minded testing before it reaches production. Because the services we touch are rate-limited, a smart proxy rotation and multi-account management layer is already sketched; you will turn that draft into a fault-tolerant component that cleanly hands off to the vision modules. Finally, no feature is done until it lives on the server automatically. You should feel at home scripting deployments, spinning containers, wiring CI/CD and monitoring—whatever keeps the releases hands-free and repeatable. Deliverables I expect from each milestone: • Clean, well-documented Python modules (with C++/Java extensions when justified) • Working vision pipeline covering detection, processing and recognition on sample footage • Functional data-extraction layer proven against live endpoints, complete with security test logs • Proxy + account manager ready for load testing • Automated deployment scripts and concise runbook If that blueprint matches your skill set and you enjoy pushing code that sees, learns and scales, let’s talk details and time lines.