Within the Creator Highlight collection, TDS Editors chat with members of our group about their profession path in information science and AI, their writing, and their sources of inspiration. As we speak, we’re thrilled to share our dialog with Sara Nobrega.
Sara Nobrega is an AI Engineer with a background in Physics and Astrophysics. She writes about LLMs, time collection, profession transition, and sensible AI workflows.
You maintain a Grasp’s in Physics and Astrophysics. How does your background play into your work in information science and AI engineering?
Physics taught me two issues that I lean on on a regular basis: the way to keep calm once I don’t know what’s taking place, and the way to break a scary downside into smaller items till it’s not scary. Additionally… physics actually humbles you. You be taught quick that being “intelligent” doesn’t matter should you can’t clarify your pondering or reproduce your outcomes. That mindset might be essentially the most helpful factor I carried into information science and engineering.
You lately wrote a deep dive into your transition from an information scientist to an AI engineer. In your each day work at GLS, what’s the single largest distinction in mindset between these two roles?
For me, the largest shift was going from “Is that this mannequin good?” to “Can this technique survive actual life?” Being an AI Engineer shouldn’t be a lot concerning the excellent reply however extra about constructing one thing reliable. And truthfully, that change was uncomfortable at first… but it surely made my work really feel far more helpful.
You noted that whereas an information scientist would possibly spend weeks tuning a mannequin, an AI Engineer may need solely three days to deploy it. How do you steadiness optimization with velocity?
If we now have three days, I’m not chasing tiny enhancements. I’m chasing confidence and reliability. So I’ll deal with a stable baseline that already works and on a easy solution to monitor what occurs after launch.
I additionally like transport in small steps. As a substitute of pondering “deploy the ultimate factor,” I feel “deploy the smallest model that creates worth with out inflicting chaos.”
How do you assume we may use LLMs to bridge the hole between information scientists and DevOps? Are you able to share an instance the place this labored effectively for you?
Information scientists communicate in experiments and outcomes whereas DevOps people communicate in reliability and repeatability. I feel LLMs can assist as a translator in a sensible approach. As an illustration, to generate checks and documentation so what works on my machine turns into “it really works in manufacturing.”
A easy instance from my very own work: once I’m constructing one thing like an API endpoint or a processing pipeline, I’ll use an LLM to assist draft the boring however essential components, like take a look at circumstances, edge circumstances, and clear error messages. This hurries up the method lots and retains the motivation ongoing. I feel the secret is to deal with the LLM as a junior who’s quick, useful, and infrequently fallacious, so reviewing every thing is essential.
You’ve cited research suggesting an enormous development in AI roles by 2027. If a junior information scientist may solely be taught one engineering ability this 12 months to remain aggressive, what ought to or not it’s?
If I needed to decide one, it might be to discover ways to ship your work in a repeatable approach! Take one mission and make it one thing that may run reliably with out you babysitting it. As a result of in the actual world, the most effective mannequin is ineffective if no person can use it. And the individuals who stand out are those who can take an thought from a pocket book to one thing actual.
Your current work has centered closely on LLMs and time collection. Trying forward into 2026, what’s the one rising AI matter that you’re most excited to put in writing about subsequent?
I’m leaning increasingly more towards writing about sensible AI workflows (the way you go from an thought to one thing dependable). Apart from, if I do write a couple of “scorching” matter, I need it to be helpful, not simply thrilling. I need to write about what works, what breaks… The world of knowledge science and AI is stuffed with tradeoffs and ambiguity, and that has been charming me lots.
I’m additionally getting extra interested in AI as a system: how totally different items work together collectively… keep tuned for this years’ articles!
To be taught extra about Sara’s work and keep up-to-date along with her newest articles, you possibly can observe her on TDS or LinkedIn.
