Securing the Intersection of MLOps and Adversarial Robustness
I engineer the immune systems for AI applications. While the industry optimizes for generative capabilities, I optimize for governance, latency, and attack surface reduction. My work ensures that ML pipelines survive hostile environments and production realities.
- Defense in Depth: AI models are software; they require the same hardening as any critical infrastructure.
- Observability is Security: You cannot secure what you cannot monitor. I build pipelines that scream when they drift.
- Pragmatism > Hype: I focus on reproducible infrastructure and deterministic outcomes over "magic" black boxes.
| Domain | Technologies |
|---|---|
| Infrastructure & Containerization |    |
| MLOps & Pipelines |    |
| Data Engineering |    |
| Security & Scripting |    |
- Adversarial Defense: Mitigating prompt injection, data poisoning, and model inversion attacks.
- System Hardening: Securing Linux environments for high-throughput inference (WSL/Ubuntu).
- Traffic Analysis: Monitoring real-time data flow for intrusion detection signatures.
System Status: 🟢 Online | Latency: 12ms | Encryption: AES-256