Job Placement Digest

Personal Statement

I enjoy building production AI systems that are reliable, maintainable, and designed around the needs of the user rather than the capabilities of a model. My approach to software engineering is to think about the entire system—from data ingestion and retrieval to inference and evaluation—instead of treating the language model as the solution. I believe successful AI products come from well-designed infrastructure, deliberate architectural decisions, and continuous validation against real-world use cases.

One project that reflects this approach is Botzy, an AI-powered chatbot platform for small businesses. I designed it using a Retrieval-Augmented Generation (RAG) architecture so responses could be grounded in business-specific documents instead of relying solely on an LLM. During testing, I found that most failures originated in the retrieval pipeline rather than the model itself. That led me to iterate on embedding models, document chunking, retrieval parameters, and overall system design. I prioritized trustworthiness over raw speed, accepting additional latency and system complexity to improve the reliability of the chatbot’s responses. This experience reinforced that production AI is an infrastructure problem as much as it is a machine learning problem.

I’m currently seeking an Applied AI Engineer or AI Software Engineer role where I can help build and deploy production AI systems. Over the next several years, my goal is to grow into a Machine Learning Infrastructure Engineer or AI Systems Architect, designing AI platforms that prioritize reliability, observability, and user trust while enabling scalable, production-ready AI applications.