music

// hello, world

Daniel Flores Araiza

ML Engineer & PhD Researcher in Explainable AI with 5+ years building production Deep Learning systems. 15+ peer-reviewed publications · Best Paper Award CVPR 2022 · Available for freelance & consulting.

Get In Touch Download CV

About Me

I'm an ML Engineer and Data Scientist with 5+ years of experience building, deploying, and optimising Deep Learning solutions — from academic research to production pipelines. Currently finishing my PhD in Computer Science at Tecnológico de Monterrey, specialising in Explainable AI (XAI) applied to medical image classification.

My work bridges rigorous research and real-world engineering. I've trained CNN-based XAI models achieving state-of-the-art interpretability benchmarks, fine-tuned LLMs using LoRA/QLoRA, built ML quality assurance pipelines, and taught 80+ students across university courses and industry bootcamps.

I'm actively pursuing roles and freelance opportunities in ML engineering, LLM applications, and MLOps — where I can bring both scientific depth and production-minded pragmatism. Advanced English; comfortable in international async and meeting environments.

5+ Years in ML/AI
15+ Publications
80+ Students Trained
1🏆 Best Paper Award

Education

🎓
Ph.D. in Computer Science Tecnológico de Monterrey · Aug 2020 – Dec 2025
Explainable Deep Learning · XAI · CNNs · PyTorch
🎓
M.Sc. in Intelligent Systems Tecnológico de Monterrey · Jan 2014 – Jun 2019
🎓
B.Sc. in Mechatronics Engineering Tecnológico de Monterrey · Aug 2008 – Dec 2013

Work Experience

6 roles across industry, research, and education

Data Scientist

Current

Grupo CAPEM

Jul 2025 – Present

  • Led migration of credit data from legacy processes into a centralised Python/Pandas pipeline, cutting validation time by ~60% and ensuring regulatory compliance.
  • Defined KPI, OKR, and Balanced Scorecard requirements; supported analytics platform evaluation and vendor selection.
  • Designed domain-specific custom GPT models with automated n8n workflows, reducing manual reporting effort and enabling near-real-time insights.
Python Pandas GPT n8n KPI/OKR

Higher Education Instructor

Current

Tecnológico de Monterrey

Feb 2024 – Present

  • Delivering hands-on Computational Thinking courses to 30+ students per semester, integrating Python, Docker, and GitHub in practical projects.
  • Teaching advanced LLM fine-tuning techniques (LoRA, QLoRA) using Unsloth, bridging academic research with industry-ready inference optimisation.
Python Docker LoRA / QLoRA Unsloth LLMs

ML Pipeline QA Consultant

Independent

Aug 2024 – Jul 2025

  • Designed and executed integration and performance test suites (Pytest, Grafana K6) for ML model pipelines, catching 95%+ of regressions before client release.
  • Collaborated with ML teams to formalise quality standards and acceptance criteria for production model deployments, directly informing MLOps process improvements.
Pytest Grafana K6 MLOps CI/CD

PhD Researcher – Explainable AI & Medical Imaging

Tecnológico de Monterrey

Aug 2020 – Dec 2024

  • Developed CNN-based classification and XAI models in PyTorch for medical image analysis, achieving state-of-the-art interpretability benchmarks across 3 published datasets.
  • Managed end-to-end experiment lifecycle with Docker and Git, enabling reproducible research across 15+ publications and 2 international workshop presentations.
  • Won Best Full Paper at the LatinX in CV Workshop @ CVPR 2022 — selected from 100+ global submissions.
PyTorch XAI CNNs Docker Medical Imaging

Online Deep Learning Instructor

PLAi – Jalisco Open Platform for Innovation

Apr 2024 – Aug 2024

  • Trained 50+ intermediate-level students in PyTorch, Docker, and deployment pipelines, with a course completion rate above 80%.
PyTorch Docker Deep Learning

Control Center IT Specialist

T-Systems México

Apr 2016 – Aug 2020

  • Monitored and maintained high-availability IT infrastructure for enterprise clients including Audi MX, sustaining 99.9%+ uptime on critical systems.
  • Coordinated cross-functional incident response, reducing MTTR by ~30% through improved escalation workflows.
IT Infrastructure Incident Management High Availability

Technical Skills

Tools and technologies I work with

🧠 ML & Deep Learning
PyTorch scikit-learn Unsloth LangChain n8n
📊 Data & Pipelines
Pandas MySQL FastAPI DVC
☁️ MLOps & Cloud
MLFlow Docker GitHub Actions Azure Replit
🤖 LLM & GenAI
LoRA / QLoRA RAG Prompt Engineering Fine-tuning
📈 Visualisation
Matplotlib Plotly Streamlit Grafana K6 Datadog
💻 Languages
Python (5+ yrs) R C / C++

Research Publications

15+ peer-reviewed publications · Best Paper @ CVPR 2022 · View on Google Scholar ↗

Deep Prototypical-Parts paper preview
XAI · CVPR 2023

Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and Are Competitively Robust to Photometric Perturbations

Read Paper →
Causal Scoring paper preview
Causality

Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo Kidney Stone Images

Read Paper →
MACFE paper preview
Causality

MACFE: A Meta-learning and Causality Based Feature Engineering Framework

Read Paper →
In Vivo Kidney Stones paper preview
Medical Imaging · IEEE

On the In Vivo Recognition of Kidney Stones Using Machine Learning

Read Paper →
FAU-Net paper preview
Medical Imaging

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

Read Paper →
Two-step Transfer Learning paper preview
Medical Imaging

Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning

Read Paper →
Metric Learning kidney stone paper preview
Metric Learning

A Metric Learning Approach for Endoscopic Kidney Stone Identification

Read Paper →
SuSana Distancia paper preview
Metric Learning

SuSana Distancia is all you need: Enforcing Class Separability via Two Novel Distance-Based Loss Functions for Few-Shot Classification

Read Paper →
Guided Deep Metric Learning paper preview
🏆 Best Paper · CVPR 2022

Guided Deep Metric Learning

Read Paper →

Featured Projects

Selected work from research and personal projects

🧠

Prototypical Parts for Kidney Stone XAI

CNN-based prototypical part networks for morphological kidney stone classification with built-in interpretability and robustness to photometric perturbations. Published at CVPR 2023.

Python PyTorch XAI Medical Imaging
🎮

StarCraft 2 — Q-Learning Agent

Reinforcement learning agent using Q-Learning applied to the real-time strategy game StarCraft 2. Explores game-state representations and reward shaping strategies.

Python Reinforcement Learning Q-Learning
🔬

Complexity Measurement in Multi-Agent Systems

NetLogo simulation for measuring emergent complexity in Multi-Agent Systems (MAS), exploring agent interactions and quantifying behavioural entropy metrics.

NetLogo Multi-Agent Systems Complexity Theory
🚁

Quadrotor Identification and PID Control

Hierarchical position and orientation control for the Parrot AR Drone quadrotor, including system identification from experimental data and Kalman filter-based sensor fusion. Published in IEEE.

Control Systems PID Kalman Filter UAV

Awards & Certifications

Awards & Recognition

🏆
Best Full Paper Award

LatinX in Computer Vision Research Workshop, CVPR 2022 — selected from 100+ global submissions

📄
15+ Peer-Reviewed Publications

Computer vision, XAI, medical imaging, causality, metric learning — View on Google Scholar

🎤
Workshop Organiser

LatinX in AI @ CVPR 2022 & ICCV 2023

Certifications

Generative Adversarial Networks (GANs) DeepLearning.AI Specialisation · 2023
AI for Medicine DeepLearning.AI Specialisation · 2022
Neural Networks and Deep Learning DeepLearning.AI Specialisation · 2021

View full certificate list on LinkedIn ↗

Get In Touch

Available for Freelance & Consulting

Interested in ML engineering, XAI consulting, LLM fine-tuning, or ML pipeline QA? I'm open to remote work and international projects. Let's talk.

ing.daniel.bin@gmail.com