I am an infrastructure professional focused on bridging the gap between traditional networking and AI-Augmented Operations (AIOps). I specialize in developing enterprise-grade automation tools that integrate Large Language Models (LLMs) with relational databases to create self-documenting, resilient, and intelligent network ecosystems.
I am currently documenting my transition from CLI-driven management to AI-driven orchestration.
| Project | Focus Area | Technology Stack |
|---|---|---|
| ai-network-advisor | AIOps & LLMs | Google GenAI (Gemini 2.0), JSON Schema, Python |
| net-inventory-db | Persistence | SQLite, Relational Data Modeling, SQL |
| secure-net-config | Security | Python-Decouple, Secret Management, DevSecOps |
| network-asset-manager | OOP | Object-Oriented Design, API Interaction, State Mgmt |
- AI Orchestration: Implementing
application/jsonresponse filtering via LLMs for machine-readable diagnostics. - Closed-Loop Remediation: Linking AI diagnostic engines to SQL databases for automated incident logging and auditing.
- Modern Tooling: High proficiency in UV (package management), Git/GitHub workflows, and Python 3.12+.
- Data Persistence: Designing relational schemas to migrate volatile infrastructure data into permanent storage.
- ✅ Day 13: Automated JSON-to-Ticket generation for incident management.
- ✅ Day 12: Successfully refactored legacy diagnostics to the Gemini 2.0 Flash SDK.
- ✅ Day 11: Implemented SQL persistence to move from RAM-based scripts to Disk-based databases.
- Current Objective: Mastering Structured Output and Self-Healing network workflows.
- Focus: Reducing MTTR through intelligent, LLM-driven infrastructure diagnostics.