18+ years in Network Performance & Operations, transitioning into AI/ML Engineering.
After managing critical network operations serving millions of users for 18 years, I learned what breaks at scale. I pivoted into AI/ML engineering to address these bottlenecks, building and operating 12 systems over 14 months, spanning data engineering, model deployment, and agentic automation.
Autonomous Network Operations: Observe -> Decide -> Act
A cohesive production-grade suite solving the "Mean Time to Recovery" bottleneck.
- Incident Commander: Async log analyzer using Gemini 2.0 Flash Lite to process 500 logs/sec. Reduces 3,000 raw logs to 1 incident report (63x noise reduction).
- NOC-Oracle: RAG-powered troubleshooting assistant with Gemini 2.0 Flash. Uses hybrid search (Vector + Keyword) to achieve 100% retrieval accuracy and 0% hallucinations.
- Net-Ops Agent: Agentic AI using Gemini 2.0 Flash Function Calling. Enforces 100% human-in-the-loop approval for all operational actions.
2. Trailing Edge LIVE
Production Algorithmic Trading Bot
- Status: Live 24/7 on Cloud VPS.
- Tech: Python Async, WebSockets, Systemd, Ed25519 Auth.
- Strategy: Proprietary dynamic trailing take-profit with exponential decay and regime-aware auto-compounding.
- Architecture: Handles real-time market data with sub-100ms latency. Orchestrated via systemd for automatic recovery. Proves ability to ship and maintain always-on infrastructure.
End-to-End Pipeline: Synthetic Generation -> ML Analytics
A complete data science ecosystem combining physics-based simulation with rigorous ML.
- Digital Twin: Physics-based generator producing 5.6M sessions with SINR/latency modeling. 100% reproducibility.
- QoE Analytics: ML pipeline (XGBoost/LightGBM) achieving R-squared 0.9997 (driven by highly correlated physics settings) and identifying congestion bottlenecks (Cohen's d = -2.75).
- RATU Trading Suite: 4-component crypto infrastructure (Multi-chain Scanner, On-Chain Holder Analytics, Market Data REST API, FIX Bot).
- AI Studio: Generative AI interior design app (Next.js 14 + Gemini Multi-Model) delivering photorealistic renders from architectural sketches.
- Telecom ML Framework: Open-source v1.0.0 framework for telecom data science covering 6 use cases (Churn, RCA, Anomaly, QoE, Capacity, Optimization).
| Domain | Technologies |
|---|---|
| Architecture | Python Async, Systemd, Linux VPS, Event-Driven, Microservices |
| AI Models | Google Gemini 2.0 Flash/Lite, Gemini 2.5 Flash Image, Gemini 3.0 Pro Image Preview |
| AI Engineering | RAG (ChromaDB), LangChain, Agentic Patterns, Pydantic, Function Calling |
| ML & Data | XGBoost, LightGBM, SHAP, Pandas, Parquet, Synthetic Data (SDV) |
| Protocols | REST, WebSockets, FIX 4.4, JSON-RPC, GraphQL |
| DevOps | Docker, CI/CD, Pytest, Ruff, Mypy, UV Manager |
- 18+ Years of Domain Authority: Operating large-scale networks: I've lived the pain points, not guessed them. I bring this experience into the systems I now automate.
- Real-World Design: My systems handle actual constraints: partial outages, race conditions, and rate limits. "Happy path" code does not survive production.
- Measurable Business Impact: I translate technical complexity into outcomes that matter: 12 production-grade systems architected and deployed in 14 months.
- Observability & APM: Automated remediation and log analysis.
- Fintech & Trading: Low-latency decision systems.
- Industrial AI: Digital twins and predictive maintenance.
- Location: Remote Preferred | Based in Indonesia (UTC+7)
- LinkedIn: linkedin.com/in/adityonugrohoid
- Email: adityo.nugroho.id@gmail.com