Hi, I'm Kenneth! I'm currently on a career break, but normally my daily tasks revolve around a healthy mix of software, DevSecOps, data, and analytics engineering. I am also a Microsoft-certified Azure AI Engineer, and a Google Cloud Certified Professional Cloud Architect. I graduated with a degree in Applied Physics from the University of the Philippines, and for my undergraduate thesis, I joined the Instrumentation Physics Laboratory with a research concentration on signal, video, and image processing. Outside of work, a like to dabble in a bit of everything like photography, music, gaming, mechanical keyboards, to name a few. Feel free to contact me for inquiries, collaborations, consultations, hiring, and other concerns!
Work Experience
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Led a cross-functional team of 7 engineers to architect and develop the UNICEF Giga DataOps Platform, a fully open-source master data management platform built in coordination with Giga, a UNICEF-ITU joint initiative whose mission is to connect all schools to the internet by 2030. V1.0 of the Platform was launched in September 2024 and was presented at the Open Source Summit in Vienna later that month. At launch, the Platform managed data for over 2.1M school records across 141 countries, with near-real-time connectivity data for 93k schools updating every few minutes. The Platform was also being leveraged by 2 downstream applications with 5k monthly users.
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Developed a data analytics platform for a foreign agricultural company: optimized the data lake setup for near-realtime updates of logistic movements and validation of the delivery of burnt produce. Daily tasks involved orchestration and scheduling of ELT tasks and SQL queries via Dagster, and automating replication of hybrid cloud deployment environments via Terraform.
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Developed GenAI applications for several enterprise clients in the financial sector, leveraging Azure OpenAI. Daily tasks involved designing responsive chat interfaces, and image processing to remove non-text elements in financial documents in order to prepare them for vector embedding for use in downstream RAG applications.
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Developed the SDG Impact Tool for Gold Standard to digitize their process of reporting, validating, verifying, and tracking the contribution of project activities to the UN Sustainable Development Goals. Daily tasks involved backend development in Django, frontend development in React, and automating infrastructure provisioning across multiple Google Cloud deployment environments using Terraform.
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Developed an internal geospatial analytics platform for a local telecommunications company: involved in migrating user interface components to a modern tech stack and increasing test coverage.
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Developed the API and infrastructure for an inventory management and profit calculator system deployed on DigitalOcean: integrated with PayMongo API to process customer payments and AWS Cognito as a B2C identity provider.
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Software Engineer
Demand Science / Cobena Business Analytics & StrategyNov 2020 – Nov 2022 • 2 years 1 month-
Developed a unified frontend enabling users to experiment with deployed ML models being developed in-house. -
Developed the frontend for an internal data lakehouse platform allowing users to perform queries on access-controlled data, apply filters, save filters, and export their search results. - Guided the adoption of MLOps principles within the AI/ML team.
- Spearheaded the company-wide adoption of modern frontend tooling using Figma and TypeScript.
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Developed a supply chain performance survey tool for UK-based marketing consultancy Ideas and Action: led backend development with Django, Redis, and PostgreSQL, deployed to an AWS environment. -
Developed Gateway, a B2B SaaS geospatial analytics platform: led weekly code reviews to maintain quality standards and optimize the team’s delivery. -
Developed internal tooling for an FMCG client: initiated the migration from manual testing to automated API testing using Python scripts, reducing testing time from 2 days to 30 minutes.
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Processing and analysis of videos, images, and other signals using conventional signal processing methods, as well as experimental methods such as compressive sensing and machine learning.
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Developed the API for a COVID-19 contact-tracing mobile application written with Express and MongoDB.
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Underwent training in bioinformatics; developed a command-line tool and web application for designing primers for site-directed mutagenesis.
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Corps Non-Commissioned Officer for Documentation
University of the Philippines Reserve Officers' Training CorpsAug 2018 – May 2019 • 8 monthsOfficial photographer of the UPD ROTC Corps of Cadets for the C.Y. 2018-2019.
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Student Assistant
Philippine Coral Reef and Mangrove Remote Sensing Project
Dec 2018 – Jan 2019 • 1 monthPerformed ground-truth validation of images involving corals classified by a deep neural network.
Certifications
- Microsoft
Azure AI Engineer Associate
🕑May 2024 - Google Cloud
Professional Cloud Architect
🕑Jul 2021
Education
- National Institute of Physics
University of the Philippines
🕑Aug 2015 – Jul 2020B.S. Applied Physics (Major in Instrumentation)
Thesis: Compressive sensing: Applications from 1-D to N-D
Publications
- Proceedings of the Samahang Pisika ng Pilipinas 38 SPP-2020-4C-04
Compressively sampled speech: How good is the recovery?
🕑Sep 2020 – Sep 2020Modern signal acquisition technologies are made possible by the Nyquist-Shannon sampling theorem (NST). However, this paradigm is extremely wasteful as the signal is compressed before storing it by systematically discarding imperceptible information. Compressive sensing (CS) aims to directly sense the relevant information. Current literature focus either on formulating more computationally-efficient algorithms, or methods which improve the reconstruction quality. In this paper, we quantify the reconstruction quality of compressively sampled speech with a perceptually intuitive metric―the Perceptual Evaluation of Speech Quality (PESQ)―and with the standard average segmental SNR (SNRseg). The quality of recovery of compressively sampled speech evaluated using PESQ is dependent on the compression ratio, and independent of the number of subbands used to represent the signal in the spectrogram domain.
- Proceedings of the Samahang Pisika ng Pilipinas 37 SPP-2019-PB-38
Frequency domain reconstruction of stochastically sampled signals based on compressive sensing
🕑May 2019 – May 2019The field of compressed sensing (CS) has recently been gaining traction as a viable workaround to the Nyquist-Shannon sampling theorem (NSST). This allows highly accurate signal recovery from incomplete frequency information. In this paper, we investigate the ability of compressive sampling to recover the higher harmonics of a recorded guitar signal. Sampling is done in the temporal domain, and the reconstruction is performed in the frequency domain. It is shown that even when taking a small number of random samples corresponding to some underlying sub-Nyquist rate, the base frequency, including up to fifth-order harmonics, can be recovered. The performance of three algorithms, namely least absolute shrinkage and selection operator (LASSO), orthogonal matching pursuit (OMP), and smoothed L0 norm (SL0) algorithm in terms of computation time and reconstruction error (cosine similarity) were investigated.