Results-oriented machine learning engineer with several years of experience building and scaling ML infrastructure, including offline inference platforms and distributed systems. Proven ability to drive technical vision, mentor engineering teams, and deliver impactful solutions leveraging large language models (LLMs), MLOps, containerization, and orchestration technologies. Experienced in project management, personnel training, development and mentoring of young engineers.

In recent projects, I worked with several technologies and programming languages including but not limited to Python, Common Lisp and C/C++. One recent projects in described below.

• I designed and implemented a large-scale integrated scientific application (~265 KLOC in Common Lisp) and consists of several modules including machine learning, optimization, and a physics simulation modules. The application is implemented using a monolithic repository (monorepo) model. The repository is not hosted publicly but I can show other summaries and visualizations of the repository. For example, here is a Gource visualization of the repository (last update 05/27/2022) and sloccount analysis ( sloc report). Also, here is a Python notebook showing one of the applications of the tool for performing remote physics simulations (flow modeling). The tool was used to generate the results described at this blog . Finally, we will share the results of the project at a software engineering conference.

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