Security-Tech Custom ML AI - 23/01/2026 22:01 EST
Budget: $1,500 – $3,000 USD
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.
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.