Carlos Noe Lopez Mejia

BMW — Cloud DevOps Engineer (Working Student)

Jun 2025 – Sep 2026

• Design and maintain AWS infrastructure as code across two production services: Terraform for the Road Ahead Backend (Pothole Map / Dangerous Curve Assistant) and AWS CDK for the Charging Enhancement Layer (CPCL), plus AWS CLI for operational tasks, building reusable modules/constructs and staged environment configurations (int/dev/prod) with controlled backend/state management and rollout steps.
• Build and support batch data pipelines, including EMR/Spark job orchestration, AWS Glue, Python, Scala, and Spark job artifacts publishing for enterprise-scale autonomous driving and electromobility data workloads.
• Configure IAM roles and policies for cross-service access, secure data publishing, and least-privilege permissions across environments; contribute to broader security hardening (KMS, Secrets Manager, VPC).
• Publish new data-product workflows, including semantic data distribution patterns for downstream consumers in the US and EMEA.
• Operate and improve observability/monitoring infrastructure, including alerting thresholds with Athena, Step Functions, QuickSight, and defect analysis, to strengthen platform reliability.
• Produce architecture documentation, implementation proposals, and technical handoffs; support incident/investigation work with root-cause analysis and evidence-based reporting.

Ocumeda — Data Engineer and Analytics (Working Student)

Jun 2024 – Jun 2025

• Collaborated closely with stakeholders, including C-level executives (CTO), to gather and translate business requirements into scalable data solutions.
• Designed and implemented a scalable data architecture on AWS (S3, Lambda, Redshift, Step Functions) using Airbyte and Metabase to support the startup's rapid growth.
• Developed around 10 ETL pipelines using Python, connecting different data sources as external APIs and SharePoint, some resulting in a 400% reduction in execution time.
• Established data architecture best practices and standards, ensuring data quality, consistency, and governance across the organization.
• Managed the data infrastructure that supported data-driven decision-making for over 15 users, including C-level executives.

ITESM — Part-Time University Lecturer

Feb 2022 – Sep 2023

• Invited lecturer at the Master's in Applied Artificial Intelligence (MNA-V) 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 (MNA-V).
• Part-time lecturer in Python, OOP, unit/integration testing with Pytest, packaging with Python, API REST with FastAPI, Software Engineering, Monitoring, Observability, Docker, Docker Compose, Terraform, and GitHub Actions, applied to an MLOps certification course.

Wizeline — Data Engineer

Jan 2022 – Apr 2023

• Developed one streaming and two batch pipelines to extract, transform, and load (ETL) data from multiple data sources to a system recommendation using Python, both GCP (Cloud Functions, Cloud Storage, Scheduler, Cloud SQL, Pub/Sub, BigQuery, Dataflow, Scala) and AWS (EMR, PySpark, S3, Athena, Lambda, CLI).
• Taught Terraform content about data engineering in an external boot camp.
• Taught Kafka fundamentals in an external course on streaming content.
• Designed and implemented multiple unit and integration tests with pytest.
• Created a REST API for a machine-learning model using FastAPI, Python, Docker, and AWS EC2.

iVoy — Data Engineer

Feb 2021 – Jan 2022

• Collaborated in DE architecture design and implementation; POCs on batching and streaming (Apache Beam) using GCP and AWS, orchestration (Airflow), data preprocessing (Spark).
• Created a streaming pipeline and production deployment processing all packages per day using Apache Beam, BigQuery, and Dataflow.
• Consolidated data from MySQL, Postgres, and MongoDB into a Kafka cluster (500M+ records and continuous data streams).
• Created a Data Lake for raw data in AWS S3, consumed with Glue, Athena, and QuickSight.

Uber Technologies Inc — Driver and Rider Operations Intern

Oct 2019 – Jan 2021

• Data collection, cleaning, exploration, and analysis on large datasets about drivers, partners, and vehicles across 56 Mexico Rides operational cities using SQL (Presto), Python, Pandas.
• Productized a fully automated GUI application to automate pre-processing of driver/vehicle/partner documents, saving 40+ hours per month using Python, Django, and SQL.
• Converted a manual spreadsheet into an automated compliance dashboard using Python, SQL, and Google Sheets.
• Developed a document processing automation prototype using Deep Learning (ResNet-50) and OCR (81% accuracy across four categories).

Delta Smith — Data Engineering and Architect (Freelancing)

Sep 2021 – Sep 2023

• Designed data roadmaps for 2022 and 2023 covering security, observability, monitoring, orchestration, data governance, DataOps, data quality, privacy, and compliance.
• Led data projects from requirements mapping to database modeling and delivery.
• Generated three 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.

Salsuki — Community Management Platform

2024 – Current

• Sole full-stack developer and product owner for a community management platform serving a non-profit salsa school with 60+ active registered users, including bookings, skill levels, wallet balances tracked as system of record.
• Built the frontend with React 19, TypeScript, Vite, and Tailwind CSS 4 (PWA, mobile-first) and the backend on Supabase (Postgres with Row-Level Security, serverless Edge Functions in Deno, Auth, Storage).
• Deployed on Vercel with per-PR preview environments, GitHub Actions for lint/build/migration validation, and Sentry for production error monitoring.
• Designed a credit-based internal economy with asynchronous bank-transfer reconciliation via CSV processing.
• Built class/event booking with capacity limits, waitlists, and skill-level gating, plus an integrated learning-resource library and an interactive knowledge-graph view of dance-figure prerequisites.
• Run an AI-agent-orchestrated engineering workflow: scope structured tickets, dispatch implementation to Claude Code agents on isolated branches, and review every PR before merge, sustaining ~15–20 shipped tickets/month as a solo maintainer.
• Link: TODO — add live app or public repo link

DevOps Course — Development & Deployment of a GenAI-Powered Web Application

Apr 2025 – Aug 2025

• Designed and implemented the RAG and Generative AI subsystem for LECture-bot, an intelligent course material assistant.
• Built a Python-based LangChain microservice to process and embed uploaded documents, store/query vectors in Weaviate, and integrate with cloud-based and local LLMs.

Seminar Course — Natural Language Processing: Methods and Applications

Apr 2025 – Aug 2025

• Independent research on adversarial attacks targeting large language models, surveying vulnerabilities and mitigation strategies.
• Produced a seminar paper and presentation on securing LLMs against malicious exploitation.

Technical University of Munich

Oct 2023 – Expected Apr 2027

M.Sc. in Informatics. Coursework: Cloud Computing, Cloud Information Systems, Cloud-Based Data Processing, DevOps: Engineering for Deployment & Operations, Machine Learning, Requirements Engineering, Software Engineering.

Instituto Politécnico Nacional

Jan 2015 – Jul 2020

Bachelor of Computer System Engineering. Coursework: Artificial Intelligence, Software Engineering, Business Administration, Distributed Computing, Microcontrollers, Databases.

Universidad de Caldas

Jan 2019 – Jul 2019

Student Exchange in Computer System Engineering. Coursework: Machine Learning.

MLOps & Development

• Infrastructure & CI/CD: Docker, Terraform, AWS CDK, Git, GitHub Actions
• Testing & APIs: Pytest, FastAPI, Django
• Monitoring & Observability Practices

Machine Learning & AI

• Libraries: Scikit-learn, TensorFlow, Keras, OpenCV
• Domains: Deep Learning (CNN, ResNet), NLP, Computer Vision (Image Analysis, OCR, Segmentation), nnU-Net

Late Blight Segmentation in Tomatoes Using Spatial Domain Methods and Colour Analysis

Oct 2020 — International Journal of Scientific Research

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