Richmond Umagat
AVP - Platform Architect Lead (Data Streaming Platform, Enterprise API Platform)
Metro Manila, Philippines
Summary
👤
Senior🎓
Top SchoolRichmond Umagat is an AVP and platform architect lead with nearly two decades of experience designing low-latency, high-throughput systems across banking, electronics, and IoT domains. He specializes in real-time data streaming and enterprise APIs, having led Confluent/MongoDB-based platform initiatives that modernized core banking integrations and payment gateways while managing multi-million dollar SaaS contracts and cloud costs. Richmond combines deep hands-on skills—from kernel drivers and embedded networking to AWS serverless, Kafka streams, and Flink—with strategic governance, vendor management, and regulatory know-how (PCI-DSS, AML). He also prototypes AI-enabled solutions (face recognition, speech synthesis) in open-source projects, reflecting a rare blend of device-level engineering and enterprise-scale architecture. Based in Metro Manila, he pairs an applied-math background and an MBA with a track record of turning complex requirements into operational, auditable platforms.
9 years of coding experience
17 years of employment as a software developer
Master's degree Business Administration - MBA, Master's degree Business Administration - MBA at Ateneo Graduate School of BusinessBachelor's degree Applied Mathematics major in Computational Science - BS AMC, Bachelor's degree Applied Mathematics major in Computational Science - BS AMC at Ateneo de Manila University
Github Skills (10)
scikit10
machine-learning10
python10
scikit-learn10
tensorflow9
deeplearning-ai9
face-detection9
deep-learning9
opencv9
keras9
Programming languages (5)
Github contributions (5)
richmondu/libfaceid
Nov 2018 - Aug 2019
libfaceid is a research framework for prototyping of face recognition solutions. It seamlessly integrates multiple detection, recognition and liveness models w/ speech synthesis and speech recognition.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:2 releases, 289 commits, 200 pushes in 8 months
Contributions summary:Richmond primarily contributed to the development of face recognition solutions within the `libfaceid` framework. Their commits focused on expanding the model's capabilities by adding and improving various classifier models, including Naive Bayes, SVM, and Neural Networks, within the `encoder.py` file. The user also made modifications to the `facial_recognition.py` and `facial_recognition_training.py` files, which suggests an involvement in integrating these classifiers within the overall facial recognition pipeline and setting up the training process. These changes enhanced the framework's ability to identify and classify faces with more sophisticated algorithms.
ctcface-recognitionsynthesisspeech-recognitionprototyping
richmondu/libpycognito
Jul 2019 - Aug 2019
Contributions:39 commits, 36 pushes, 1 branch in 1 month
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