A public, evidence-driven plan to evolve from fundamentals and projects in 2026 toward AI Engineering and MLOps, focused on the United States market, by 2030.
The choice is made. The destination is building, evaluating, deploying, and operating AI systems in production. The realistic path runs through a strong foundation of Python, data, and cloud — aiming at the United States market.
01 / Primary track
Take machine learning models and LLMs from prototype to production, with responsible evaluation, deployment, and operations.
02 / Entry foundation
The practical base that supports AI in production and is the most pragmatic way into the track.
03 / Trust layer
Treat security and responsibility as part of the AI system, not a separate final step.
04 / Differentiator
Turn experience in video, customer service, and product thinking into clear documentation, demos, and collaboration.
The long-term view guides decisions, while daily work stays limited to the next verifiable set of deliveries.
Choose the primary track, publish strong projects, and prove software, cloud, and delivery fundamentals.
Work in a real context, own deliveries, and document decisions, impact, and operations.
Deepen specialization, lead initiatives, and compete for international opportunities.
Each stage defines observable outcomes. Certifications and technologies support the plan; projects, experience, and impact remain the main evidence.
Turn learning into proof that can be opened, tested, and discussed.
Secure an internship, junior role, or freelance engagement involving software, cloud, data, security, or automation.
Become responsible for a feature, service, or project across delivery, operations, and evolution.
Move into a higher-responsibility role across cloud, platform, security, data, or solutions.
Work in AI Engineering and MLOps at an international level, focused on the United States market.
These items exist today and will be updated as new deliveries replace intention with proof.
Case study for an offline-first PWA with maps, weather, and synchronization.
Application using interactive maps, Firebase, and local event curation.
Python experiment with a planned path toward automation, data, and ML.
Technical resume in Portuguese and English, also available as PDF.
The plan will be reviewed when projects, experience, and opportunities produce better evidence. Technologies may change; the commitment remains focused on fundamentals, delivery, and verifiable learning.