I design and deploy high-performance computer vision systems for real-world industrial environments.
- ⚡ Real-time pipelines (30+ FPS) under constrained hardware
- 🧠 GPU acceleration (CUDA / TensorRT / zero-copy)
- 🏭 Industrial vision systems (inspection, automation, edge deployment)
- 🔧 Robust perception under non-ideal conditions (lighting, noise, reflections)
- 🚀 Optimized production pipelines from ~14 → 30 coins/sec
- ⚙️ Designed lock-free, zero-copy architectures for deterministic performance
- 🧠 Built multi-stage CV pipelines: segmentation → orientation → classification → defect detection
- 📡 Deployed systems on Jetson (edge AI) with real-time streaming and multi-client support
I specialize in systems where hardware meets AI, focusing on:
- Deterministic real-time processing
- GPU memory optimization (cudaHostRegister, zero-copy)
- Scalable vision architectures (multi-process, shared memory)
- Industrial communication (CAN / J1939 / Modbus)
Real-time inspection system built for high-throughput production lines.
- 🧩 C++ camera producer + Python consumers (shared memory)
- ⚡ Zero-copy GPU pipeline
- 🔄 Lock-free double buffering (0 ms overhead)
- 🧠 CV pipeline: segmentation → alignment → classification → defect detection
- 📡 WebSocket streaming to multiple clients
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from typing import Dict, List
class Engineer:
def __init__(self):
self.name = "Gerald Cainicela"
self.role = "Embedded & CV Engineer"
self.location = "Lima, Peru 🇵🇪"
self.focus = [
"Real-Time Computer Vision",
"Edge AI & Inference Optimization",
"Industrial Embedded Systems",
"Protocol Engineering (CAN/J1939)"
]
def get_stack(self) -> Dict[str, List[str]]:
return {
"vision": ["CUDA", "TensorRT", "YOLO"],
"embedded": ["ESP32", "FreeRTOS", "STM32"],
"edge": ["Jetson", "Docker", "OpenVINO"],
"plc": ["CODESYS", "IEC 61131-3", "Modbus"],
}
async def deploy(self, target: str) -> str:
return f"Optimized & deployed to {target}" |
#include <memory>
#include <vector>
#include <string>
#include <map>
class Engineer {
const std::string name_{"Gerald Cainicela"};
const std::vector<std::string> focus_{
"Real-Time CV", "Edge AI",
"Industrial IoT", "Protocol Eng."
};
public:
auto getStack() const noexcept {
return std::map<std::string,
std::vector<std::string>>{
{"vision", {"C++20", "CUDA", "TensorRT"}},
{"embedded", {"ESP-IDF", "FreeRTOS", "ARM"}},
{"edge", {"Jetson", "Docker", "ONNX"}},
{"comms", {"CAN", "J1939", "Modbus"}}
};
}
template<typename T>
[[nodiscard]] auto optimize(T&& pipeline) {
return std::make_shared<std::decay_t<T>>(
std::forward<T>(pipeline));
}
}; |
Specialized in real-time computer vision and industrial embedded systems. I build high-performance pipelines for edge inference, industrial bus communication, and robust production deployments.
🔬 Computer Vision & Inference
⚙️ Edge AI & System Orchestration
🧪 Labs & Experimentation
🏭 Industrial Automation & PLC
🎓 Academic & MATLAB
📄 Publishing & Templates
🛠️ Tech Stack
Computer Vision & AI
C++14/17/20 CUDA cuDNN TensorRT ONNX Runtime OpenCV OpenCV C++ YOLO YOLOv8 DeepStream Qt6 OpenGL libtorch Eigen Manim
Edge AI & Deployment
Jetson Orin Jetson Nano JetPack L4T Docker NVIDIA Runtime TensorRT DeepStream ONNX OpenVINO
Embedded & IoT
C C++ ESP32 ESP-IDF FreeRTOS MicroPython Arduino STM32 ARM Cortex Raspberry Pi LoRa (SX127x/SX126x) MQTT Modbus RTU CAN J1939 RS485 RS232 OTA Bootloader
Industrial Automation & PLC
CODESYS IEC 61131-3 Ladder Logic Structured Text Function Block Diagram Modbus TCP/RTU OPC UA SCADA
Protocols & Hardware Interfaces
I2C SPI UART PWM ADC DMA BLE WiFi ESP-NOW NVS WebSocket HTTP TCP/UDP Ethernet USB Profibus Profinet
ML/DL Frameworks
PyTorch libtorch TensorFlow TensorFlow Lite ONNX Keras OpenCV DNN scikit-learn NumPy Pandas Plotly Matplotlib
DevOps, Tools & Languages
Docker docker-compose Kubernetes CI/CD GitHub Actions GitLab CI Linux Ubuntu Git CMake Makefile Bash GStreamer FFmpeg GDB Valgrind Perf NVIDIA Nsight MATLAB LaTeX
Databases
PostgreSQL MySQL SQLite SQL Server
CAD & Design
AutoCAD SolidWorks EasyEDA
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Lima, Peru · 2026