Machine Learning Engineer

Angel Carrillo-Bermejo is a machine learning engineer specializing in applying deep learning to solve real-world problems. He earned his BSc in Mechatronics Engineering from Universidad Modelo and an MSc in Computer Science from Universidad Autónoma de Yucatán (UADY). He began a PhD at Universidad Nacional Autónoma de México but chose to leave the program to focus on deploying AI systems in production.

About Me

Angel Carrillo-Bermejo has experience spanning applied research and production machine learning systems. During his PhD studies at the Universidad Nacional Autónoma de México (UNAM), he worked on a thesis developing a dissimilarity measure using chain codes in open curves, establishing mathematical foundations with potential applications in medical signals and imaging. He contributed to projects detecting anomalies in ocular blood vessels in premature and diabetic patients and explored machine learning approaches for identifying patterns in brain tumors and analyzing signals linked to conditions such as schizophrenia and Alzheimer’s disease.

Transitioning to industry, he has developed production-ready deep learning models for audio DeepFake detection and child grooming detection, designed flexible voice moderation pipelines with configurable analysis of age, speech, and emotion, and led large-scale infrastructure migrations to reduce operational costs. He also improved transcription throughput by optimizing language models to support scalable, high-performance systems. His work reflects a commitment to using AI to address critical challenges and deliver solutions with real-world impact.

“Nobody ever figures out what life is all about, and it doesn't matter. Explore the world. Nearly everything is really interesting if you go into it deeply enough.”― Richard P. Feynman.

Research interest

Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and are widely deployed in academia and industry. I am interested in applying these techniques to solve complex, real-world problems across sectors such as content moderation, security, healthcare, finance, and other domains where trustworthy and scalable AI systems can create value. My experience includes developing production systems for DeepFake audio detection, building child grooming detection algorithms, designing configurable voice moderation pipelines, optimizing transcription workflows to improve performance and efficiency, and creating deep learning models for medical imaging tasks like MRI data augmentation and tumor classification. I am passionate about contributing to innovative projects that leverage AI to drive impact in diverse industries.
You can find my all design work on GitHub, or if you wanto to look on my Profile on Linkedin. If you want to communicate with me, please send me an email or contact.

Software

Pandas

90%
90% Complete

Python

90%
90% Complete

TensorFlow

85%
85% Complete

Scikit-learn

80%
80% Complete

PyTorch

85%
85% Complete

TorchAudio

80%
80% Complete

Docker

70%
70% Complete

Transformers (HuggingFace)

85%
85% Complete

HTML

75%
75% Complete

AWS Services

75%
75% Complete

Java

70%
70% Complete

Conferences

Professional Experience

  • August 2022 - Present
  • Modulate
  • Machine Learning Engineer Multilingua
  • August 2021 - 2022
  • Apziva
  • Machine Learning Scientist
  • August 2018 - 2022
  • Universidad Nacional Autónoma de México
  • Research Scientist, Computer Vision on Medical Image
  • Aug 2020 - Dec 2021
  • Universidad Modelo
  • Research Scientist, Medical Signals Processing and Medical Image
  • Jan 2020 - Aug 2020
  • Universidad Nacional Autónoma de México
  • Research Scientist, Computer Vision on Medical Image
  • Jan 2017 - Dec 2017
  • APTEM
  • Jr Developer, iOS
  • Jun 2016 - Aug 2016
  • Texas A&M University
  • Research Scientist, Aerospace Engineer for Autonomous Vehicles

Education Experience

  • Aug 2018 - Jun 2022
  • Universidad Nacional Autónoma de México
  • Doctor of Philosophy in Computer Science
  • Aug 2016 - Aug 2018
  • Universidad Autonoma de Yucatán
  • Master of Science in Computer Science
  • Aug 2012 - Aug 2016
  • Universidad Modelo
  • Bachelor of Science in Mechatronics Engineering

Publications

Jerra, V.S., Ramachandran, B., Shareef, S. et al. Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma. Med Oncol 41, 198 (2024).


A chain code for representing high definition contour shapes. E Bribiesca, F Bribiesca-Contreras, Á Carrillo-Bermejo, Journal of Visual Communication and Image Representation 61, 93-104 2 2019.


An Approach to the Computation of the Euler Number by means of the Vertex Chain Code. E Bribiesca, UD Braumann, A Carrillo-Bermejo, H Sossa-Azuela, Computational and Mathematical Methods in Medicine 2020.


Retraining random forest algorithm for lower limb prosthesis tracking using an RGB-D camera J Perez-Gonzalez, A Carillo-Bermejo, N Hevia-Montiel, JC Huegel, 15th International Symposium on Medical Information Processing and Analysis 2020.


Slope-chain-code-based characterization of Trypanosoma cruzi in blood smear images. A Carrillo-Bermejo, N Hevia-Montiel, E Bribiesca, P Haro. 15th International Symposium on Medical Information Processing and Analysis 2020.


Características morfométricas en dominio discreto para reconocimiento de tumores cerebrales. A Carrillo-Bermejo, NH Montiel, E Molino-Minero-Re Res. Comput. Sci. 147 (7), 319-333, 2018.


Discrete tortuosity as a morphometric measure in brain tumors. N Hevia-Montiel, E Molino-Minero-Re, AJ Carrillo-Bermejo. Revista mexicana de ingeniería biomédica 38 (1), 188-198 2017.

Research Projects

Contact

If you want to communicate with me, please send me an email.