🛡️ AI Security & Infrastructure Engineer

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.

🧠 Core Philosophy

• 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.

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🛠️ Engineering Stack

Domain	Technologies
Infrastructure & Containerization
MLOps & Pipelines
Data Engineering
Security & Scripting

🔬 Key Competencies

• 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.

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📊 Activity

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📡 Uplink

_{System Status: 🟢 Online | Latency: 12ms | Encryption: AES-256}