AI Solutions Engineer | Telecom & Industrial AI | Agentic RAG, Multi-Agent Systems, LLM Observability

Led network optimization at Huawei for 18 years. Pivoted. Now I design and deploy AI systems shaped by decades of knowing what breaks on live networks, from multi-agent RAG pipelines to LLM observability platforms and telecom-domain ML at scale.

Multi-Agent RAG System Built From First Principles

A multi-agent RAG system built from scratch - no LangGraph, LlamaIndex, CrewAI, or AutoGen. Each version introduced progressively deeper autonomy, culminating in AgentLens with a 6-layer observation model and dual-tab pipeline debugger (live NDJSON streaming + post-mortem trace). Includes a 25-page documentation site.

• Multi-Agent Pipeline: ReAct reasoning loop with hybrid retrieval (vector + BM25), two-agent orchestrator with Quality Judge that evaluates chunks and retries with feedback, per-role model selection across 4 pipeline stages
• Evolution: v1 (25 tests) -> v2 (69 tests) -> v3 (208 tests) -> AgentLens (263 tests), each version adding deeper autonomy
• Infrastructure: 21 containers on a single EC2 m7i-flex.large via Terraform IaC, Cloudflare SSL, nginx routing to 4 apps
• Observability: 6-layer observation model (Prompt -> Thinking -> Raw -> Parsed -> Execution -> Observation), NDJSON streaming debugger, per-tool timing, confidence scoring
• Live: agentlens.adityonugroho.com

Islamic Knowledge Retrieval for the Indonesian Muslim Community

Retrieval backbone for pAIjo - a WhatsApp/Telegram-based Islamic knowledge assistant. Collaboration with Ainun Najib (project lead, Singapore-based data platform & civic tech leader). The core design constraint: fabricating or misattributing Islamic quotes is a critical failure mode, so RAG ensures every response is grounded in verified, curated content from trusted Islamic scholars.

• Architecture: FastAPI server -> OpenAI embeddings -> Qdrant vector database (68 curated chunks across 3 categories: Fiqih & Traditions, Ramadan Guidance, General Islamic Q&A)
• Performance: ~100ms query latency, 25 concurrent connections stable, 100% retrieval coverage verified
• Hallucination-Resistant Design: Responses grounded in verified Islamic scholarly content via RAG retrieval rather than relying on LLM training data

Enterprise RAG Platform + Phased Local LLM Stack

Two tracks of RAG and LLM infrastructure. An enterprise RAG platform with multi-provider support, and a phased local LLM stack where all phases share a single Ollama runtime via a common Docker network.

• Enterprise RAG Platform: 4-microservice architecture (API Gateway, Ingestion, Retrieval, Query) with 5 LLM providers (Ollama, OpenAI, Anthropic, Azure, Vertex AI), Streamlit UI with side-by-side Direct LLM vs RAG comparison, and PII detection with automatic redaction
• RAG Operator Console: 4-layer PromptAssembler with token budgeting (4096 tokens), operator debugging UI for prompt assembly and chunk visualization, 2-turn clarification context
• Ollama Multi-LLM Server: Multi-model hot-swap switching, side-by-side model comparison, automated benchmarking, 3-tier model selection (fast/balanced/quality)
• Ollama Runtime: Shared GPU-accelerated Ollama container on dedicated Docker network, centralized model storage, lifecycle independence from downstream phases

AI-Powered Network Operations: Observe -> Decide -> Act

A three-component AI system designed to reduce Mean Time To Repair (MTTR) in Network Operations Center workflows. Each component maps to one phase of the operational decision cycle:

• Incident Commander: Event-driven log analyzer with tumbling window batching (5s or 100 items) and async architecture for non-blocking I/O. Uses semantic root cause clustering via Gemini 2.0 Flash Lite with Pydantic-enforced structured outputs for real-time incident detection. 63x noise reduction via tumbling-window aggregation.
• NOC-Oracle: RAG-powered troubleshooting with hybrid search combining semantic vector search and keyword boosting for exact code matching. Features context-aware chunking preserving error-solution relationships and hallucination-resistant retrieval via strict context enforcement and source citations.
• Net-Ops Agent: Agentic AI with reasoning-action separation pattern and human-in-the-loop approval gates. Uses deterministic function calling from pre-defined toolbelt with Pydantic validation ensuring type safety. All actions require explicit authorization.

Classical ML Applied to Telecom: 6 End-to-End Use Cases + Framework

Two tracks grounded in 10+ years of network operations domain expertise. The telecom-ml-framework provides 6 use case specifications with problem framing, data requirements, and model architectures. The telecom-ml-portfolio links 6 independent, end-to-end implementations with domain-informed synthetic data generators embedding real telecom physics.

• Implementation Results (telecom-ml-portfolio):

  Use Case	ML Type	Algorithm	Result
  Churn Prediction	Binary Classification	XGBoost	AUROC: 0.86
  Root Cause Analysis	Multi-class Classification	XGBoost	Acc@1: 0.91
  Anomaly Detection	Unsupervised	Isolation Forest	F1: 0.70
  QoE Prediction	Regression	LightGBM	RMSE: 0.45
  Capacity Forecasting	Time-Series	LightGBM+Prophet	MAPE: 14.5%
  Network Optimization	Reinforcement Learning	Q-Learning	+61% vs random

• Telecom ML Framework: 6 production-ready ML project templates with complete specs, domain-informed data generator patterns, unified standards (SHAP-compatible versions, CI/CD, pytest)

• Key Differentiator: Telecom domain knowledge embedded at every stage - synthetic data uses physics-based models (SINR, Shannon capacity, congestion patterns), SHAP interpretability connects model outputs to operational decisions

End-to-End MLOps: Synthetic Generation -> Model Training -> Strategic Insights

• Digital Twin: Deterministic multi-table generator producing 50K users, 2K cells, ~5.6M sessions with cascade-based seeding for bit-exact reproducibility, referential integrity validation, and Parquet columnar storage
• QoE Analytics: Six-phase pipeline - R²=0.7247 (XGBoost), ROC-AUC=0.9645 (LightGBM), SHAP interpretability, Cohen's d effect size analysis (d=-2.12 for congestion on QoE)

3-Step Interior Design Render Pipeline on Two Platforms

• Cloud Run: Streamlit + Vertex AI (gemini-2.0-flash-lite text, gemini-2.5-flash-image sketch/render), Docker + Cloud Build CI/CD. Live App
• Vercel: Next.js 14 + TypeScript, 3 API routes, gemini-3-pro-image-preview for near-exact sketch-to-render correspondence. Live App

OpenClaw Dashboard: AI Agent Observability

• Single-file SPA (zero build dependencies) with FastAPI backend + Tailwind CSS (CDN) + Alpine.js. 9 views covering agent sessions, configuration, security audit, and system resources. Smart data filtering, glassmorphism UI, localhost-bound for security.

Algorithmic Trading + Multi-Chain Analytics + Latency Research

• Trailing Edge: Async Python trading bot for Binance - dynamic trailing take-profit, regime detection (BASE/QUOTE auto-switch), Donchian channel hard stop, Ed25519 auth, 24/7 systemd with Telegram alerts
• RATU Suite: FIX 4.4 market making (3 sessions), REST market analytics (7 endpoints), on-chain whale tracking (6 chains), DEX pair scanning (4 chains)
• Binance Colo Research: Singapore vs Tokyo latency testing - 4.4x improvement from Tokyo VPS (307ms vs 1,344ms avg), 80 concurrent workers, interactive HTML reports

Battle-Tested Playbook for Multi-App VPS Deployment

Deploy 21+ containerized apps on a single VPS with Docker — from zero to production.

• Architecture: Docker Compose, Nginx Reverse Proxy, Bash, SSH Automation
• Key Win: Image layering strategy (500MB base / 2.5GB ML tier) reducing pull bandwidth by 5x across 15 services
• Covers: VPS hardening, Docker networking, subdomain routing, selective deployments, monitoring & alerting

• LinkedIn: linkedin.com/in/adityonugrohoid
• Email: adityo.nugroho.id@gmail.com