AI/ML Engineer - Telecom/Industrial Domain Expert | 18+ Years Network Optimization Leadership (Deputy GM at Huawei)

After leading network optimization strategy for operators serving millions, I witnessed what breaks at scale and built AI systems to fix it. I pivoted into AI/ML engineering to address these bottlenecks, architecting and deploying 12 end-to-end systems in 14 months (Nov 2024-Jan 2026), including AI/ML systems (GenAI, RAG, Agentic AI, MLOps, AI Observability) and production infrastructure.

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

A cohesive integrated suite solving the "Mean Time to Recovery" bottleneck with three integrated components deployed as part of NOC operations:

• 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.
• 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 strict context enforcement preventing hallucination.
• 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.

Multi-Step GenAI Workflow for Architectural Design on Google Cloud

Cloud-native Streamlit application demonstrating advanced GenAI workflow orchestration using Google Vertex AI. Transforms simple text descriptions into photorealistic architectural renders through a 3-stage pipeline: text enhancement -> sketch generation -> photorealistic rendering.

• Text Enhancement Stage: Uses Gemini 2.0 Flash Lite to expand simple user descriptions into detailed architectural specifications, preparing enriched context for downstream image generation stages
• Sketch Generation Stage: Gemini 2.5 Flash Image creates architectural line drawings from enhanced text descriptions, creating intermediate visual representations for rendering pipeline
• Photorealistic Rendering Stage: Transforms sketches into V-Ray style photorealistic renders using advanced prompting techniques for realistic architectural visualization
• Cloud Run Deployment: Containerized Streamlit application deployed on Google Cloud Run with Vertex AI integration and Docker containerization for scalable cloud deployment
• Live Service: View App (Cloud Run)

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

A complete MLOps ecosystem combining deterministic data generation with ML workflows. Generates 50K users with 5.6M sessions in reproducible fashion, then applies six-phase analytics pipeline from EDA to strategic recommendations.

• Digital Twin: Deterministic multi-table generator with bit-exact reproducibility via cascade-based seeding. Produces 4 output tables (users, cells, sessions, events) with referential integrity validation. Parquet columnar storage with embedded schema metadata for efficient storage.
• QoE Analytics: Six-phase analytics pipeline achieving R²=0.7247 (XGBoost, MAE=0.3672, RMSE=0.4560) and ROC-AUC=0.9645 (LightGBM, precision=0.46, recall=0.92). Features SHAP interpretability for model explainability and Cohen's d effect size analysis for quantifying impact magnitude of QoE drivers.

High-Performance Async Trading Bot with Dynamic Trailing Take-Profit

High-performance asynchronous Python trading bot for Binance featuring dynamic trailing take-profit strategies, regime detection, Donchian channel gating, and Ed25519 authentication. Built for 24/7 autonomous operation on low-cost infrastructure with low-latency reaction times.

• Dynamic Trailing Take-Profit: Exponential decay from start to min factor, automatically adjusting profit targets as gains increase, locking in profits while allowing upside
• Regime Detection and Auto-Compounding: Automatic switching between BASE (holding inventory) and QUOTE (holding cash) modes with all-in compounding for bull rallies
• Donchian Channel Hard Stop: Volatility-aware stop-loss using Donchian Channels with intelligent re-entry gating that waits for price to cross mid-channel in favorable direction after stop triggers
• 24/7 Systemd Deployment: Deployed as systemd service for autonomous operation with auto-restart, resource limits, and journald logging

Real-time Automated Trading Unified: Multi-Chain Crypto Infrastructure

A comprehensive 4-component crypto trading infrastructure for multi-chain token discovery, on-chain analytics, market data, and low-latency order execution:

• FIX Bot: FIX Protocol 4.4 connector with ED25519 authentication and defensive message parsing. Features three FIX sessions (Market Data, Order Entry, Drop Copy) with spread market making strategy for low-latency order execution
• REST API: Comprehensive market analytics using 7 Binance public endpoints with multi-timeframe kline analysis (1h, 4h, 1d) and connection pooling. Dataclass-based response parsing ensures type safety
• On-Chain Monitor: Token holder analytics and whale tracking across 6 chains (BSC, Ethereum, Polygon, Arbitrum, Base, Avalanche) with three-mode architecture (Basic Info, Top Holders, Full Snapshot) and known label identification
• Moon Radar: Multi-chain DEX pair scanner supporting 4 chains (Ethereum, BNB Chain, Polygon, Base) with dynamic pair parsing and early token detection via 24h price gains

Framework for Telecom AI/ML Solutions

A framework for building AI/ML solutions to real-world telecom challenges, emphasizing domain expertise and practical problem-solving. Provides 6 ML project templates covering the most common telecom AI/ML use cases with complete technical specifications.

• Six Use Case Specifications: Complete specs for Churn Prediction, Root Cause Analysis, Anomaly Detection, QoE Prediction, Capacity Forecasting, Network Optimization with problem framing and model recommendations
• Domain-Informed Data Generators: Hand-crafted generators embedding real telecom physics (SINR, Shannon capacity, congestion patterns) vs generic synthetic data, demonstrating domain expertise
• SHAP-Compatible Standards: Enforced dependency versions (numpy<2.0, xgboost<2.0) ensuring interpretability works without conflicts, critical for business adoption
• Unified Project Structure: Consistent template across all projects with config, data generation, features, models, notebooks, and tests enabling rapid project creation

• Telecom/Industrial AI Specialist: 18+ years domain expertise in large-scale network optimization (10M+ subscribers) combined with hands-on AI/ML engineering
• Production Systems Focus: Building AI/ML solutions that solve real operational problems in telecom and industrial domains
• Transferable Expertise: Network optimization -> Industrial AI, Observability, Infrastructure systems

• Telecom/Industrial AI: Domain-specific AI/ML solutions leveraging network optimization expertise
• Observability & APM: Automated remediation and log analysis.
• Fintech & Trading: Low-latency decision systems.
• Location: Remote Preferred | Based in Indonesia (UTC+7)

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