Independent Technical Validation of AI / Computer Vision Platform (Python, Docker, AWS)

We are seeking an independent senior software engineer or machine learning engineer to perform a technical validation of an existing AI/computer vision platform. This is NOT a development project. We are not looking for someone to improve the system, retrain models, or build new features. We need an independent assessment of whether another engineer can successfully take over the project using the materials provided. The system includes: Python codebase Docker deployment AWS environment Computer vision / machine learning models Datasets Documentation Benchmarking materials Project Objective We need an independent review of the handoff package and current system to determine: Can another engineer successfully take possession of and continue development of the system? Is the documentation sufficient to install, run, and understand the platform? What is the current maturity level of the product? Scope of Work Review provided repositories, documentation, models, datasets, deployment instructions, credentials, and recorded handoff session. Validate whether: Repositories are accessible and understandable Documentation is sufficient System can be installed and run from the provided materials Test videos can be processed Overlay videos can be generated Prediction/output files can be generated Architecture is understandable Another engineer could continue development Deliverables Provide a written assessment covering: Handoff Completeness Are the materials sufficient for another engineer to continue development? Reproducibility Can the system be installed and run from the provided documentation? Architecture Review Is the architecture understandable and maintainable? Missing Materials Are any critical assets, credentials, documentation, or dependencies missing? Technical Debt What major technical limitations, risks, or unfinished work exist? Product Classification Is the current system best classified as: Prototype MVP Production-ready Future Direction Assessment Based on the current architecture, could the platform reasonably be adapted into an anomaly detection / review-assistance workflow rather than a fully automated scoring system? What major development steps would be required? Required Experience Python Docker AWS Machine Learning / Computer Vision Software Architecture Technical Due Diligence Reviewing existing codebases and developer handoffs Please include examples of similar architecture reviews, technical due diligence projects, platform audits, or handoff validations you have completed.