AI & Machine Learning
From focused predictive models to adaptable foundation models — AI and ML engineering grounded in real problems and real-world use.
From Predictive Models to Foundation Models
Machine learning and AI overlap, but they solve problems in different ways. ML engineers typically build focused predictive systems from structured data, while AI engineers adapt foundation models and generative AI to broader workflows.
In both cases, the work starts with a useful problem, not a fashionable technology. The goal is to choose the right approach, make it work reliably, and connect it to the people and systems that depend on it.
What This Covers
- Use-case discovery and AI strategy
- Data preparation and feature engineering
- Predictive model development and evaluation
- Foundation-model adaptation and prompt design
- AI/ML integration and deployment
- Monitoring, retraining, and model lifecycle management
From Model to Working System
A model is only the beginning. I help teams deploy, monitor, and improve models as data changes, whether the system predicts an outcome or supports a generative workflow.
The focus is practical: accuracy where it matters, sensible use of compute, clear feedback loops, and systems that are reliable, scalable, and useful in everyday work.
Grounded in a Real Use Case
Whether building a task-specific predictive model or integrating a foundation model into a product, I work with teams to turn a defined problem into a measured, maintainable solution.