Daniel Azemar
Computer Vision & Software Engineer — Real-Time Systems · AI · Agentic Workflows
Barcelona, Spain · dani@azemar.eu
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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: production computer vision at city scale, real-time embedded firmware,
and scientific data pipelines (ESA Euclid ground segment). Currently working
hands-on in computer vision.
Roles are described by the work and the stack rather than by employer
name, by choice. Full detail on request.
Professional experience
2026 – present · Computer Vision Engineering
Video analytics for perimeter security and environmental monitoring
- Detection and tracking models for a product where inference runs on constrained hardware in the field, not in a datacentre.
- Returning to hands-on computer vision with four years of embedded and real-time systems work added underneath it.
2022 – 2026 · R&D Software Engineering
Industrial embedded systems
- 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 · Computer Vision & AI Engineering
Environmental monitoring and smart-city systems
- 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 Engineering (internship)
Scientific data centre — ESA Euclid Mission ground segment
- Performance-sensitive validation tools improving accuracy and efficiency of astronomical data processing; data-integrity protocols under high-throughput conditions.
Education
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 — professional working proficiency (B2, Cambridge First Certificate)
Described by work and stack rather than employer name, by choice.
Full detail on request:
dani@azemar.eu.