Applied AI Engineering · Computer Vision · ML Systems
Applied Machine Learning Engineer | Computer Vision | ML Systems | LLMs & RAG
I turn complex, real-world problems into practical AI workflows—from problem formulation and model selection to implementation, validation, and product integration. Postdoctoral research in Machine Learning & AI and a Ph.D. in Mathematics strengthen my analytical thinking and help me develop evidence-based solutions when standard methods fall short.
How I work
I break down difficult problems, challenge assumptions, and design solutions that can be tested—not just described. I have built modular computer vision and RAG pipelines, evaluated trade-offs among accuracy, reliability, speed, and complexity, and collaborated with domain experts to make AI useful in real applications.
When a project needs an unfamiliar technique or a fresh perspective, I study relevant scientific literature, prototype alternatives, and use experimental evidence to guide decisions. Research is an engineering advantage, not my primary job title.
Read my approach →Selected engineering & research work
As the sole AI engineer, developed multi-view pose, segmentation, anatomical measurement, and quality-control workflows and supported integration with a real application.
Explore engineering decisions →Built retrieval, configurable inference, evaluation, tests, and observability into a modular Python-based RAG system.
Explore implementation →Extending RAG into a modular AI application, with API development, containers, orchestration, agentic workflows, and engineering practices in the planned scope.
See planned scope →Applied mathematical graph abstraction to molecular representations, exploring lower graph complexity while retaining predictive information.
Read case study →Beyond implementation
Graduate-level teaching in machine learning, graph neural networks, and optimization, alongside advising multiple M.Sc. students, has strengthened how I explain technical decisions, provide feedback, and support the development of less-experienced colleagues.
I bring self-directed learning, a strong sense of responsibility, and collaborative problem solving to challenging AI work.
Experience & education →Let's build useful AI
Explore the evidence behind my work or get in touch about applied AI engineering opportunities.
