Work experience
Computer Vision & Applied AI Engineer
INESC TEC · TRIBE Laboratory · Porto, Portugal
- Lead the design and development of end-to-end AI and computer-vision solutions for autonomous robotic systems, from sensor integration and data acquisition to model development, edge deployment and field validation.
- Serve as technical lead for AI and robotic perception, defining system architectures and technical decisions across multiple robotics projects and prototypes.
- Deliver complete AI-enabled solutions spanning embedded firmware, robotics middleware, computer vision, backend services and Android applications.
- Optimise inference for resource-constrained edge platforms, balancing latency, accuracy and computational requirements.
- Lead the technical delivery of 5+ robotic R&D demonstrators from concept to field deployment with industrial partners and multidisciplinary engineering teams.
- Work directly with industrial stakeholders to translate technical requirements into validated systems for operational environments.
Invited Professor (part-time)
ISEP-IPP · Porto, Portugal
Teaching undergraduate engineering courses in computer systems, operating systems and electrical systems.
AI & Robotics Research Engineer
INESC TEC · TRIBE Laboratory · Porto, Portugal
- Designed real-time perception pipelines combining RGB, NIR, LiDAR, GNSS and IMU sensors for autonomous navigation and robotic decision-making.
- Developed and optimised deep-learning models for object detection, instance segmentation and scene understanding using PyTorch, OpenCV and ONNX.
- Built data acquisition, annotation and evaluation pipelines that enabled the development, validation and continuous improvement of machine-learning systems.
- Developed modern C++, Python and ROS 2 software for embedded Linux and resource-constrained edge devices.
- Benchmarked AI models under changing illumination, weather and terrain conditions to improve real-world robustness.
Research Trainee
DaRTES Laboratory, Telecommunications Institute (IT) · Porto, Portugal
Contributed to an academic IoT project using ESP32 nodes and oneM2M publisher–subscriber architectures across CoAP, HTTP and MQTT.