Current Technical Focus
- Physical AI & Digital Twins: Cognitive layers for industrial assets, stateful modeling, and real-time anomaly detection.
- LangChain Ecosystem: Multi-node agent workflows with LangGraph and RAG pipelines.
- Edge AI Systems: Quantized TFLite models on ESP32/ARM, sub-100ms inference latency, and hybrid cloud-edge intelligence.
LLM & Agent Architecture Expertise
LangChain & RAG
Context-aware reasoning over live system state, vector databases (FAISS, Chroma) for technical documentation, and grounded retrieval.
LangGraph
Stateful multi-node agent workflows, event-driven reasoning loops, and Digital Twin agents with persistent memory.
Edge AI
Developing quantized TFLite models for real-time sensor telemetry on MCU-class hardware (ESP32, STM32).
Featured Project: LLM-Powered Digital Twin
A real-time Digital Twin architecture that integrates IoT sensor data with rule-based system modeling and an LLM-powered reasoning agent.
Key Capabilities
- Live Digital Twin state modeling & Health evaluation
- Natural language interaction with physical systems
- Explainable diagnostics and recommendations
- RAG-powered reasoning & LangGraph-based orchestration
Digital Twin System Architecture
Industrial Digital Twins run on a five-layer, event-driven stack that bridges low-latency field hardware with cloud-scale analytics. My hands-on production experience covers the foundation of that stack — the physical field layer through the data pipeline that feeds the twin.
1. Physical Field & Control Layer
PLC/SCADA systems, smart sensors (vibration, temperature, pressure), actuators, and embedded controllers (STM32, ESP32, FPGA).
2. Edge Processing & Gateway
Protocol normalization (OPC UA → MQTT), edge inference and aggregation, local store-and-forward caching, and device authentication (mTLS).
3. Data Pipeline & Storage Layer
High-throughput ingestion into time-series stores (InfluxDB/Timescale), spatial data (PostGIS), event streaming (Kafka/Pulsar), and object storage for CAD/BIM assets.
4. Twin Processing & Analytics Engine
Physics-based simulation and ML-driven anomaly detection, extended with LangGraph reasoning agents in my LLM-powered Digital Twin project.
5. Visualization & Operational UI
Operator-facing dashboards and natural-language interfaces that surface twin state, diagnostics, and recommendations in real time.
Technical Stack
AI & LLM Systems
- Frameworks: LangChain, LangGraph
- RAG: FAISS, Chroma, Vector Pipelines
- ML: TensorFlow, TFLite (Quantization), Scikit-learn
Physical AI & IoT
- Modeling: Digital Twins, Edge AI Frameworks
- Hardware: STM32, ESP32, ARM Cortex-M/A
- OS: Embedded Linux, FreeRTOS, Bare Metal
Networking & Backend
- Protocols: MQTT, Modbus, BACnet, TCP/IP, SIP
- Backend: FastAPI, Django, Spring Boot
- Storage: Redis, InfluxDB, PostgreSQL, MongoDB
Cloud & Dev Ops
- Infrastructure: AWS (IoT Core, Lambda), Azure
- Architecture: Microservices, Event-Driven
- Container: Docker, Kubernetes basics
Career Highlights
- Physical AI Framework (Epic Safety): Architected an Edge AI framework on ESP32 using quantized TFLite models for fire suppression, achieving sub-100ms inference latency for industrial safety.
- IoT Device Management (VTech): Engineered a global platform on AWS IoT Core, leading cross-functional teams to design high-performance device-side agents.
- Mass Deployment (NTT): Delivered 200K+ Android tablets to NTT Japan; architected custom home launchers and SIP/RTP multimedia frameworks.
- Multimedia SDK Pioneers (Varovision): Developed MPEG-2 TS demuxers and motion detection algorithms for early mobile DTV and portable media players.
Architectural Philosophy
- LLMs as Cognitive Layers: Language models should reason, explain, and assist—not replace physics or control logic.
- State Over Data: Digital Twins must model behavior and evolution, not just metrics.
- Production First: Observability, tracing, and evaluation are mandatory for real-world AI systems.
- Human-Centric AI: Systems should communicate clearly with operators and engineers.