In my 15 years as a software engineer, I have had the chance to work with a lot of different technologies. As such, I can help you build products across the full stack: machine learning / computer vision (scikit-learn, pytorch, tensorflow, keras), backend (Python), frontend (HTML, CSS, Javascript), cloud deployment (AWS, azure). Can quickly get up-to-speed with technologies not listed here.

I strongly believe that data science and machine learning benefit from practices already established in software engineering. Unit/integration tests help us build more robust products, continuous integration and deployment help avoid the "the notebook only runs on my machine" syndrome, code review helps teams build common knowledge about the data and its intricacies.

In most machine learning projects, getting more/better training data beats spending time on improving model architectures and hyperparameters. I have worked with in-house data collection/annotation (using standard + custom tooling), with crowd-sourcing and with synthetic image generation, and can help you design and execute your data acquisition strategy.

I have several years of teaching / mentoring experience, including a hands-on data science lab, that so far over 100 university+private students have taken. I would be happy to mentor any junior data scientists on your team in their day-to-day work. Also open to individual mentoring requests.