I am Carlos, I have over seven years of experience in software development and data, primarily in consultancy, logistics, and mobility.
I am passionate about solving complex business problems, including collecting, interpreting, analyzing, and modeling data. I also like learning about management and new technologies focused on Machine Learning, MLOps, Data Engineering, and Data Analysis.
I am currently pursuing a Master's degree in Computer Science at the Technical University of Munich (Machine Learning).
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I'm looking to collaborate on data science and data engineering projects.
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I also teach computer science topics such as Python, OOP, Unit and Integration Testing with Pytest, Packaging with Python, API REST, Software Engineering, Monitoring, Observability, Docker, and GitHub Actions. Visit https://carloslme.com/ide
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Ask me about data engineering topics (SQL, ETL, orchestration, Spark, streaming, batch, etc), and MLOps topics.
You can reach me at:
- Personal Email: carloslmescom@gmail.com
- TUM Email: carlosl.mejia@tum.de
Munich Germany (Oct 2023 - 2025)
Candidate for Master of Science in Informatics
Coursework: Data Science, Machine Learning, Databases
Mexico City, Mexico (Jan 2015 – Jul 2020)
Bachelor of Computer System Engineering
GPA: 8.2 / 10
Coursework: Artificial Intelligence, Software Engineering, Business Administration, Distributed Computing, Microcontrollers, and Databases.
Manizales, Colombia (Jan 2019 – Jul 2019)
Student Exchange in Computer System Engineering
Coursework: Machine Learning
ITESM - Instituto Tecnologico y de Estudios Superiores de Monterrey-, Mexico, Hybrid
- Invited lecturer at the Master in Applied Artificial Intelligence (MNA-V) at Tecnológico de Monterrey for 6 weeks in July-August. Course: Introduction to MLOps.
- Participated in the proposal and design to register the first MLOps subject as part of the ITESM master's program called Master in Applied Artificial Intelligence (MNA-V).
- Part-time lecturer in programming subjects with Python, OOP, Unit and Integration Testing with Pytest, Packaging with Python, API REST with FastAPI, Software Engineering, Monitoring, Observability, Docker, Docker Compose, Terraform, and GitHub Actions, applied to a certification course in Deployment of Machine Learning Models in Production Environments (MLOps).
Delta Smith, Las Vegas, US - Remote
- Designed Data Roadmap for 2022 (Q2 - Q4), and 2023, for Security, Observability, Monitoring, Orchestration, Data Governance, DataOps, Data Quality, Privacy, and Compliance.
- Lead data projects from requirements mapping to database modeling and delivery.
- Improve the design, documentation, maintenance, monitoring, and optimization of the existing code, and data tools.
- Generated 3 workflows orchestrated by Airflow Google Composer.
- Architected and implemented an end-to-end batch data pipeline with ScreamingFrog, Google Cloud Storage, GSheets, Docker, and Terraform.
- Architected and developed Data Observability, Logging, and Monitoring infrastructure using Google Cloud Functions, BigQuery, Data Studio, and Shipyard.
- Designed and implemented Data Security baselines, such as a training course about best practices, and authentication (MFA, 2FA).
- Implemented a batch pipeline orchestration with Shipyard, Cloud Functions, and BigQuery.
- Supported and improved Analytics Engineering team processes such as ETL and ELT.