AI Engineering Roadmap
I'm a software engineer with a background in cloud data engineering (Java, Python, GCP stack), and I'm currently pursuing UT Austin's online MSAI program (Fall 2026 start, targeting May 2028 graduation).
I work in data and cloud engineering, and I'm deepening my knowledge in ML, AI, and the math behind them. At the same time, I'm growing toward a senior engineering role with a focus on architecture and system design.
Here's the plan, broken into three phases.
Phase 1: Foundation
Now through Summer 2027. Theme: bridge the gap between data engineering and ML engineering.
Foundation (now through Summer 2027). Before coursework begins: review linear algebra and core math (Deisenroth et al.), build PyTorch fundamentals, and preview UT's Deep Learning materials. At work, keep getting better as a software engineer.
Milestones I'm committing to:
- Complete pre-program self-study: PyTorch fundamentals, linear algebra review (Deisenroth et al.), and UT's Deep Learning course materials
- Build and deploy at least one production-grade ML project (not a notebook, not a tutorial clone): Dockerized, tested, API-served, monitored
- Start writing publicly about ML engineering patterns, what I'm learning, and where data engineering meets ML infrastructure
- Contribute to at least one open-source ML infrastructure project (MLflow, Ray, vLLM, or similar)
What success looks like: by the end of this phase, my GitHub and writing should make it obvious I build ML systems.
Phase 2: Depth and Proof
Fall 2027 through May 2028. This is the heaviest stretch of the MSAI program. Deep Learning, NLP, ML foundations, and Ethics all land here. The goal is not just to pass but to come out of each course with something I can show. By graduation, I want a portfolio of 2-3 deployed projects that show range (NLP, computer vision, or recommendation systems), anchored by one capstone-quality project that covers the full ML lifecycle: data ingestion, training, serving, and monitoring.
Phase 3: Apply and Grow
Summer 2028 through 2031. MSAI is done. Portfolio is built. The goal is to own production ML systems end to end, keep publishing technical writing, and by 2031, be operating at a senior level where I'm designing systems, mentoring engineers, and shaping technical direction.
How to Hold Me Accountable
I'll update this page quarterly with progress. If a milestone slips, I'll say why. No excuses.
If you're on a similar path, feel free to reach out. If you're ahead of me, I'd love to learn from you.
Last updated: May 2026