TUM M.Sc. Informatics • Cloud, Data & AI Engineering
Carlos Mejia
Cloud & Data Engineer & Applied AI Researcher
Bridging rigorous academic AI research at Technical University of Munich with 9+ years of enterprise software and data platform architecture across AWS and GCP. Focused on production infrastructure-as-code, multi-environment data pipelines, MLOps, and agentic workflows.
- Munich, Germany
- carlos.mejia@tum.de
- GitHub
About
Carlos has more than nine years of experience in software and data development, combining consultancy, startups, and large companies across mobility, logistics, media, and healthcare. He has led teams of 2 to 3 people and is currently expanding his technical experience and knowledge with a master's degree in Informatics at TUM Munich, focused on cloud computing, machine learning, including deep learning and natural language processing, and software engineering.
He is equally comfortable in the fast-paced environment of a startup, where he has designed data architectures from scratch, and in the more structured environment of a large company, where he has built production infrastructure at scale. That combination allows him to bring strong engineering practices to small teams without slowing down the speed they need.
He is excited by complex technical and business challenges related to collecting, interpreting, analyzing, and modeling data, and increasingly with orchestrating AI agents in real engineering workflows. He also enjoys sharing his knowledge, as he has taught classes at both personal and university levels on topics such as machine learning, MLOps, data engineering, and data analysis.
Core Competencies & Architectural Pillars
Bridging enterprise-grade cloud and data platform engineering with rigorous academic AI research at TUM Munich.
Production Cloud Infrastructure & IaC
Architecting resilient multi-environment infrastructure on AWS and GCP with reproducible Terraform and AWS CDK, hardened IAM/KMS security policies, and zero-downtime deployment pipelines.
Enterprise Data Platforms & Streaming
Designing high-throughput batch and real-time streaming ETL architectures (EMR/Spark, Kafka, AWS Glue, BigQuery) processing hundreds of millions of records across hybrid cloud stacks.
Agentic AI & Knowledge Graphs
Engineering structured Knowledge Graph retrieval layers and orchestrating AI coding agents in real software delivery workflows with LangChain, Weaviate vector databases, and LLMs.
DevOps & GitOps Reliability
Establishing automated CI/CD deployment gates, local database migration test stacks in GitHub Actions, containerized Docker microservices, and Postgres RLS security architectures.
Applied AI Research (TUM Chair)
Designing and evaluating structured KG retrieval layers against benchmark architectures (RegulaRAG, LightRAG, HippoRAG 2) for regulatory test scenario generation from UN Regulation 152.
Technical Instruction & Mentorship
Teaching university master's courses at ITESM and industry workshops in MLOps, testing practices with Pytest, FastAPI microservices, and cloud data platform engineering.
Interactive Skill Matrix
Click any skill to highlight related career milestones, architecture projects, and research artifacts across the site.
☁️ Cloud & DevOps
📊 Data Engineering
🧠 AI & Software
Career & Research Timeline
Maintain AWS infrastructure-as-code with Terraform and AWS CDK across production mobility services; orchestrate batch data pipelines with EMR/Spark, AWS Glue, and Python; configure IAM and KMS security hardening.
Built and maintain a full-stack platform for a non-profit salsa community (React 19, TypeScript, Supabase, Postgres RLS, Deno Edge Functions). Orchestrated using an AI-agent-driven solo development workflow.
Designed scalable AWS serverless data architecture (S3, Lambda, Redshift, Step Functions); developed ~10 ETL pipelines with Python connecting clinical APIs with up to 400% execution speedups.
Knowledge Graph Construction & Retrieval for LLM-Based Test Scenario Generation from UN Regulation 152. Benchmarking RegulaRAG, LightRAG, and HippoRAG 2.
Architected batch data pipelines and observability infrastructure with Google Cloud Composer (Airflow), BigQuery, Docker, and Terraform for commercial clients.
Taught courses in the Master's in Applied AI and MLOps certification: Python OOP, Pytest, FastAPI, Docker, Terraform, GitHub Actions, and Observability.
Developed streaming and batch ETL pipelines across GCP and AWS for enterprise clients (NBCUniversal, Dow Jones); built FastAPI ML model microservices on Docker and AWS EC2; taught Terraform and Kafka.
Consolidated MySQL, Postgres, and MongoDB streams into a Kafka cluster (500M+ records); built streaming pipelines with Apache Beam on Dataflow and an AWS S3 data lake with Glue and Athena.
Productized a Python GUI automation app saving 40+ hours/month; built document classification prototypes using Deep Learning (ResNet-50) and OCR.
Featured Projects & Field Notes
In-depth technical write-ups, architectural trade-off evaluations, and applied research deliverables.
Salsuki: Append-Only Ledger & Postgres RLS Engine
Architecting a zero-loss credit economy (`SUM()` ledger at read time), atomic SQL `SECURITY DEFINER` RPCs, and local Supabase CI testing in GitHub Actions.
KG Retrieval for Automotive Test Scenario Generation
Designing structured Knowledge Graph retrieval layers evaluated against RegulaRAG, LightRAG, and HippoRAG 2 for regulatory test scenario extraction from UN Reg 152.
LECture-bot: Academic RAG with LangChain & Weaviate
Building multi-modal lecture indexing and hybrid vector similarity retrieval to ground LLM student responses accurately on university course materials.
Degrees, Certifications & Honors
Academic degrees from top-tier institutions, specialized multi-cloud credentials, and academic recognitions.
Academic Degrees
Technical University of Munich (TUM)
M.Sc. in Informatics • Oct 2023 – Expected Apr 2027
Focus on distributed cloud computing, machine learning, and knowledge representation. Advised research in Knowledge Graphs and Graph RAG at the Chair of Robotics, AI and Real-Time Systems.
Instituto Politécnico Nacional (UPIITA)
B.Sc. in Telematics Engineering • Jan 2015 – Jul 2020
Rigorous dual-discipline engineering curriculum covering distributed software engineering, computer network protocols, operating systems, and signal processing. Graduated in top 5% of cohort.
Professional Certifications & Appointments
Multi-Cloud & Infrastructure as Code
AWS • Google Cloud • HashiCorp
Production architecture credentials and expertise across AWS (Solutions Architecture, Serverless), GCP (Cloud Data Engineering, Composer/Airflow), and HashiCorp Terraform automation.
University Lecturer Appointment
ITESM Master's in Applied AI • 2022 – 2023
Appointed lecturer teaching graduate courses in MLOps, automated testing with Pytest, containerized FastAPI microservices, and production CI/CD deployment pipelines.