Miguel Pineda
Software Engineer at Stages Learning
Queensland, Australia
Summary
🤩
Rockstar🎓
Top SchoolMiguel Pineda is a pragmatic full-stack software engineer with over six years of experience building web and mobile applications across .NET Core, PHP (Symfony/Laravel), Node.js, Angular/React and Flutter. He has a strong emphasis on test-driven development and CI/CD, having raised frontend test coverage from 0% to 80% and implemented pipelines with Bitbucket, GitLab and GitHub Actions. Miguel has repeatedly modernized infrastructure using Terraform, CloudFormation, Kubernetes and AWS services to improve security, scalability and repeatable deployments. He’s comfortable spanning product-facing features and internal tooling, and has led small teams and mentoring efforts while acting as a hands-on contributor. Based in Queensland and currently at Stages Learning, he pairs practical delivery experience in EdTech with a growing interest in AI—pursuing a Master of Artificial Intelligence at QUT. A pragmatic tinkerer, he blends engineering rigor with a focus on measurable improvements in stability and deployment speed.
2 years of coding experience
8 years of employment as a software developer
Master of Artificial Intelligence, Computer Science, Master of Artificial Intelligence, Computer Science at QUT (Queensland University of Technology)Ingeniería, Ingeniería en Desarrollo de Software, Ingeniería, Ingeniería en Desarrollo de Software at Universidad Tecmilenio
English
Stackoverflow
36reputation
4kreached
2answers
0questions
Github Skills (27)
intel10
conda10
forge10
conda-forge9
parallel9
transformers9
docker-compose9
pytorch9
docker9
deepspeed9
huggingface-transformers9
language-model8
gpt8
scikit-learn8
helm8
Programming languages (1)
Github contributions (5)
ma-pineda/data-connector_staged-recipes
Aug 2023 - Sep 2023
A place to submit conda recipes before they become fully fledged conda-forge feedstocks
Contributions:31 pushes, 3 branches in 1 month
jafraustro/ai-containers
Jun 2024 - Jun 2024
This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch that have been optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise
Contributions:4 pushes in 1 day
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