INETUM
Senior engineer with full, end-to-end technical ownership of the vision core of an industrial visual inspection product built on the NVIDIA platform. Combines deep expertise in unsupervised anomaly detection and defect segmentation with production-grade GPU inference optimization and complete model lifecycle management. Also able to design and lead the evolution of the pipeline orchestration toward a high-performance native C++ core, delivering real-time decisions on the factory floor.
Expert-level PyTorch: CNN / transformer vision architectures, training, evaluation and rigorous ONNX export (zero train/serve skew).
One-class / unsupervised anomaly detection: PatchCore, EfficientAD, PaDiM, student–teacher, normalizing flows, and their real failure modes (reference-set contamination, threshold calibration with few or no defective samples, synthetic defects via cut-paste / DRAEM, over-rejection, drift).
Supervised defect segmentation and detection (encoder–decoder, DETR-family): training, acceptance criteria and imbalanced datasets.
Methodological rigor:
AUROC / AUPRO alongside plant-level metrics (escape rate, false-reject rate) and regression validation against recorded data.
C++ & Real-Time Systems
Expert-level modern C++ (C++17/20) for real-time vision pipelines, in addition to expert Python.
High-performance systems design: native pipeline/orchestrator coordinating capture, pre-processing, inference and post-processing while keeping data in memory and avoiding unnecessary copies and hops.
Hard latency budgets: determinism, watchdogs and graceful degradation.
Linux, Docker, Git and CI as the natural working environment.
GPU & Inference Optimization (NVIDIA)
Solid CUDA: execution model, streams, CUDA Graphs, memory management (pinned, unified, pre-allocation), writing and debugging custom kernels.
GPU libraries: cuBLAS, NPP, CV-CUDA, Thrust or equivalents for accelerated image pre-processing and scoring.
TensorRT in production: engine building,
📌 Senior Computer Vision (Madrid)
🏢 Inetum
📍 Madrid