I’m looking to commission an end-to-end machine-learning solution that bridges physical or network security with broader technology operations. The core of the project is a single, cohesive AI that seamlessly switches context between those two worlds instead of treating them as separate silos. Scope of intelligence • Threat detection & prevention – real-time identification of anomalies, intrusions, policy breaches, or suspicious behavior across logs, sensors, and traffic. • Data analysis & reporting – actionable insights, trend visualizations, and auto-generated reports that decision-makers can consume without data-science fluency. • System optimization & automation – feedback loops that tune system performance, resource allocation, and patches/updates based on the AI’s findings. Tech expectations Rapid prototyping and model experimentation can live in Python (TensorFlow, PyTorch, scikit-learn—whichever suits), while performance-critical inference or low-latency modules may be rewritten in C++. I’ll host on our existing infrastructure, so clean APIs, a modular codebase, and thorough documentation are essential. Deliverables 1. Trained models with reproducible training pipelines 2. Source code (Python & C++) and build scripts 3. REST or gRPC endpoints ready for containerization 4. Unit tests covering key logic and security edge cases 5. Deployment guide plus high-level architecture docs Acceptance criteria • Model accuracy/precision/latency targets agreed during kickoff are met or exceeded • Threat-detection component generates no more than the specified false-positive rate on our validation set • Automation routines demonstrate measurable performance gains in a controlled test If you’ve built multi-domain AI systems before—or have a strong security analytics background—let’s talk. I’m ready to dive into datasets, constraints, and timelines as soon as you sign the NDA.