About
About
I am an early-career builder pivoting intentionally into AI/ML engineering. My work is centered on applied systems: agent workflows, retrieval and note intelligence, computer vision pipelines, and engineering automation that help people do better work.
I studied Industrial-Organizational Psychology with a Data Science minor. That background shapes how I approach AI systems: with engineering curiosity, practical judgment, and a focus on usefulness.
What I Bring
- I build with an applied engineering mindset: clear problem framing, practical architecture, and honest status reporting.
- I like systems that connect models to workflows rather than treating the model itself as the entire product.
- I focus on constraints, tradeoffs, and current capability so projects stay credible and grounded.
Technical Focus
- Python for AI workflows, backend logic, automation, and data handling
- LLM systems, tool orchestration, retrieval patterns, and structured memory
- Computer vision workflows, model refinement loops, and evaluation thinking
- Data analysis, documentation, and iterative project development with Git
Why the Psychology Background Matters
My I/O Psychology background shapes how I think about adoption, decision-making, cognitive load, and whether a system meaningfully improves a user’s workflow.
That perspective is useful in applied AI work, where the challenge is often not just generating outputs but building systems that are trustworthy, usable, and worth integrating into daily work.
Current Goal
I am seeking AI/ML engineering internships and entry-level roles where I can contribute to applied AI systems, learn quickly in a strong engineering environment, and keep growing across modeling, automation, evaluation, and product-minded implementation.