🚀 AIOps Engineer | Python, Systems & Networking | Building AI for logs, metrics & incident detection

I focus on building systems at the intersection of:

• ⚙️ Infrastructure & Distributed Systems
• 🌐 Networking & Communication
• 📊 Observability (Logs, Metrics)
• 🤖 AI/ML for Operational Intelligence

• Designing a production-style AIOps system
• Building pipelines for log & metric ingestion
• Developing anomaly detection models for infrastructure
• Applying ML to incident detection and root cause analysis

Core Systems

• Python
• Linux
• Networking & Data Communication

Backend & APIs

• FastAPI (building APIs for AIOps systems)
• PostgreSQL (storing logs, metrics & incident data)

Data & AIOps

• NumPy, Pandas
• Scikit-learn (in progress)
• Time-series & anomaly detection

Engineering

• Git & GitHub
• Jupyter Lab

A lightweight observability simulator that generates system metrics (CPU, Memory, Latency), detects anomalies using statistical techniques, and correlates incidents.

A structured engineering-focused repository covering:

• Log parsing & event correlation
• Infrastructure metrics analysis
• Incident data processing
• Foundations for ML in AIOps

• AIOps Engineer/Builder
• Site Reliability Engineer (SRE)
• Platform / Systems Engineer

• Systems > Tools
• Observability is critical for reliability
• Debugging production systems is a core skill
• AI should enhance operational decision-making

Building an end-to-end AIOps system:

Logs + Metrics + ML → Actionable Operational Insights

• 💼 LinkedIn: (add your link)

🚀 Systems → Observability → AI → AIOps Engineering