Within the Creator Highlight collection, TDS Editors chat with members of our neighborhood about their profession path in knowledge science and AI, their writing, and their sources of inspiration. Immediately, 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 knowledge science and AI engineering?
Physics taught me two issues that I lean on on a regular basis: easy methods to keep calm after I don’t know what’s occurring, and easy methods to break a scary drawback into smaller items till it’s now not scary. Additionally… physics actually humbles you. You be taught quick that being “intelligent” doesn’t matter when you can’t clarify your considering or reproduce your outcomes. That mindset might be probably the most helpful factor I carried into knowledge 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 greatest distinction in mindset between these two roles?
For me, the most important shift was going from “Is that this mannequin good?” to “Can this method survive actual life?” Being an AI Engineer is just not a lot concerning the excellent reply however extra about constructing one thing reliable. And actually, that change was uncomfortable at first… nevertheless it made my work really feel far more helpful.
You famous that whereas an information scientist may spend weeks tuning a mannequin, an AI Engineer might need solely three days to deploy it. How do you stability optimization with velocity?
If we’ve got 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 method to monitor what occurs after launch.
I additionally like transport in small steps. As a substitute of considering “deploy the ultimate factor,” I feel “deploy the smallest model that creates worth with out inflicting chaos.”
How do you assume we might use LLMs to bridge the hole between knowledge scientists and DevOps? Are you able to share an instance the place this labored properly for you?
Information scientists converse in experiments and outcomes whereas DevOps of us converse in reliability and repeatability. I feel LLMs may also help as a translator in a sensible means. For example, 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: after 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 check circumstances, edge circumstances, and clear error messages. This quickens the method loads and retains the motivation ongoing. I feel the secret is to deal with the LLM as a junior who’s quick, useful, and infrequently mistaken, so reviewing the whole lot is essential.
You’ve cited analysis suggesting a large progress in AI roles by 2027. If a junior knowledge scientist might solely be taught one engineering ability this yr to remain aggressive, what ought to or not it’s?
If I needed to decide one, it will be to discover ways to ship your work in a repeatable means! Take one undertaking 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 concept from a pocket book to one thing actual.
Your latest work has targeted closely on LLMs and time collection. Wanting forward into 2026, what’s the one rising AI matter that you’re most excited to write down about subsequent?
I’m leaning increasingly towards writing about sensible AI workflows (the way you go from an concept to one thing dependable). Moreover, if I do write a few “sizzling” matter, I need it to be helpful, not simply thrilling. I wish to write about what works, what breaks… The world of knowledge science and AI is stuffed with tradeoffs and ambiguity, and that has been fascinating me loads.
I’m additionally getting extra interested in AI as a system: how completely 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.
