INETUM

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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.

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- 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

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- 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)

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- Solid CUDA: execution model, streams, CUDA Graphs, memory management (pinned, unified, pre-allocation), writing and debugging custom kerne

📌 Senior Computer Vision (Madrid)
🏢 Cv-Library
📍 Madrid