Shashwat Gokhe, software and ML consultant
I help teams build software, ML systems and apps, from the first sketch to production. Two companies built and sold. These days I spend most of my time getting ML models to run fast on small chips.
Move your cursor over the chip. Touch the chip. It's a toy systolic array. Every flash is one multiply and add.
I studied electrical engineering, so I think about software from the metal up. That comes in handy when a model has to be fast, cheap and small enough to live on a device instead of a data center. It also means I care about the boring parts: tests, deploys, and code someone else can pick up later.
Three ways to work together. Most projects end up using a bit of each.
Architecture reviews, backend systems, and the unglamorous work of making a product reliable. I can join for a focused sprint to fix what's slow or fragile, or stay on as the senior engineer you haven't hired yet.
From picking the right model to keeping it healthy in production. Fine tuning, evaluation, retrieval, agents, and inference that doesn't quietly eat your cloud budget. If it needs to run on a device, that's my favourite kind of problem.
iOS, Android and web apps, built end to end. Design, code, payments, analytics and launch. You get a product people can actually use on day one, not a demo that falls over the first time real traffic shows up.
What I'm working on
Most models are trained in the cloud and expected to stay there. I'm working on the other end: getting them to run well on edge hardware like phones, cameras, drones and embedded boards, where memory, power and heat are all in short supply.
A lot of that work comes down to precision. Every weight in a model is a number, and how many bits you spend on each one decides how much memory the whole thing needs. Pick a precision to see the difference.
Full precision. This is where training happens.
What I build when client work is done. Some of these turn into real businesses.
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