Daniel Azemar

Computer Vision & Software Engineer — Real-Time Systems · AI · Agentic Workflows
Barcelona, Spain · dani@azemar.eu

LinkedIn · GitHub · StackOverflow · Plain-text CV · Structured profile

Engineer with around eight years taking vision and sensing systems from research into production under real physical constraints. Three converging tracks: space and scientific data pipelines (ESA Euclid ground segment), production computer vision at city scale, and real-time embedded firmware.

Professional experience

2023 – present · R&D Software Engineer

Industrial embedded-systems manufacturer

Firmware in C/C++ for embedded microcontrollers (low-level, real-time), with latency as a key design driver.
SQL/NoSQL database design for hardware–software data integration, improving query efficiency.
Real-time systems and algorithm optimisation: threading, schedulers, latency-critical workloads.
Cross-platform C#/.NET server infrastructure.

2018 – 2022 · Software, Computer Vision & AI Engineer

Smart Cities / Industry 4.0 solutions company

Led Industry 4.0 and Smart Cities projects end-to-end, from research to production deployment.
Real-time computer vision for traffic, safety and environmental monitoring: detection, tracking, image enhancement and classical processing pipelines.
Trained and fine-tuned a wide range of state-of-the-art vision architectures for detection and classification.
Deployed 30+ edge-AI camera networks processing 100k+ daily transactions; scalable pipelines over 300+ TB of self-built datasets; shared edge/cloud framework and monitoring GUIs.

2017 · Software Engineer (intern)

Scientific data centre — ESA Euclid Mission

Performance-sensitive validation tools improving accuracy and efficiency of astronomical data processing; data-integrity protocols under high-throughput conditions.

Education

2020 – 2022 · UAB — Mathematics coursework: linear algebra, statistical modelling, optimisation, calculus.
2018 – 2020 · UAB — MSc in Computer Vision. Thesis: “From haze to smoke: weakly-supervised smoke detection”.
2014 – 2018 · UAB — BSc in Computer Science. Thesis: “Temporal resolution enhancement of multi-spectral image sequences”.

Skills

Languages: C/C++ (C++11/17, Linux/Windows), Python (NumPy, OpenCV, PyTorch), C#/.NET, SQL, MATLAB, JS/HTML/CSS, Bash, PowerShell.
AI / ML: detection & classification architectures, CNNs, transformers, PyTorch, TensorFlow, Keras, scikit-learn, ONNX, fine-tuning, pruning, few-shot learning, optical flow.
Data: SQL/NoSQL, ETL pipelines, large-scale dataset management, automated labelling, distributed training, Weights & Biases, NumPy, SciPy, Astropy.
Systems: RTOS, microcontrollers, CUDA, edge GPUs, thread scheduling, latency tuning, board bring-up, hardware–software integration.
Infrastructure: Git, Docker, CI/CD, Linux, Windows, Cloudflare Workers/Pages/KV/D1, LaTeX.
Agentic workflows: AI-assisted engineering with agentic coding tools (Claude Code, Copilot, Codex) integrated into daily practice — from prototyping to production automation.

Languages

Catalan — native · Spanish — native · English — fluent (Cambridge First Certificate)

Employer names and some technology specifics are omitted from the public version by choice. Full CV available on request: dani@azemar.eu.