Robotics & Embedded AI Engineer
I design the hardware, firmware, perception, and control layers behind autonomous and industrial systems—then validate them where they have to work.
ROS2 · PX4 · STM32 · Computer Vision · Industrial Automation
Selected system 01 / 06 · RoboticsAutonomous Drone System for Copper Cable Recovery and Infrastructure InspectionDeveloped an autonomous PX4-based drone platform for utility-infrastructure inspection and copper-cable recovery using Mask R-CNN segmentation, monocular metric depth estimation, and vision-guided autonomous positioning.View case study Selected case studies
A cross-section of autonomous systems, industrial AI, electronics, and robotics—documented through constraints, architecture, ownership, and outcomes.
Developed an autonomous PX4-based drone platform for utility-infrastructure inspection and copper-cable recovery using Mask R-CNN segmentation, monocular metric depth estimation, and vision-guided autonomous positioning.
Conducted industrial AI research on image and video dehazing using transformer-based architectures, diffusion-assisted reconstruction, optical flow, and hybrid dual-domain neural networks during a Mitacs internship at MacDon Industries.
Co-founded a robotics startup focused on development of industrial robotic arms, autonomous manipulation systems, ROS2-based robot control, EtherCAT motion systems, and computer-vision-guided robotics for industrial and cinematic applications.
Led the research and development of industrial IoT telemetry systems based on STM32 embedded hardware, GSM communication, industrial Modbus networking, and cloud-connected monitoring infrastructure, with more than 2000 deployed devices across industrial and medical applications.
Developed an industrial electromagnetic slag-detection platform for molten-steel casting optimization using FPGA-based signal analysis, custom six-layer PCBs, industrial PLC systems, and real-time electromagnetic sensing.
Developed a locally deployed AI assistant integrated into this portfolio website using Retrieval-Augmented Generation and an NVIDIA Jetson AGX Orin for on-device inference.
One connected engineering stack
The strongest systems are designed as a whole: sensing, electronics, real-time control, intelligence, and deployment working together.
Sensors, PCB design, STM32/ESP32 firmware, signal integrity, bring-up.
Real-time loops, PLC/VFD systems, EtherCAT, Modbus, ROS2 coordination, validation.
Computer vision, industrial perception, ML pipelines, LLM/RAG diagnostic workflows.
Manufacturing transition, field reliability, troubleshooting workflows, operational AI handoff.
Capability map
Deep embedded and hardware foundations, extended through robotics, perception, industrial automation, and applied AI.
Technical writing
Practical writing on embedded AI, PCB design, robotics, real-time systems, and the decisions behind dependable deployment.
Build something that has to work
Let's talk about robotics, embedded systems, industrial perception, or a technically difficult system that crosses disciplines.