Within the Creator Highlight collection, TDS Editors chat with members of our neighborhood about their profession path in information science and AI, their writing, and their sources of inspiration. At this time, 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: how one can keep calm once I don’t know what’s occurring, and how one can break a scary drawback into smaller items till it’s not scary. Additionally… physics actually humbles you. You be taught quick that being “intelligent” doesn’t matter for those who can’t clarify your considering or reproduce your outcomes. That mindset might be probably 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 every day work at GLS, what’s the single largest distinction in mindset between these two roles?
For me, the most important shift was going from “Is that this mannequin good?” to “Can this technique survive actual life?” Being an AI Engineer is just not a lot concerning the good 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 steadiness optimization with velocity?
If now we have three days, I’m not chasing tiny enhancements. I’m chasing confidence and reliability. So I’ll concentrate on a stable baseline that already works and on a easy technique 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 information scientists and DevOps? Are you able to share an instance the place this labored nicely for you?
Knowledge scientists communicate in experiments and outcomes whereas DevOps people communicate in reliability and repeatability. I feel LLMs might help as a translator in a sensible manner. 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 elements, like take a look at circumstances, edge circumstances, and clear error messages. This hurries up the method so much and retains the motivation ongoing. I feel the hot button is to deal with the LLM as a junior who’s quick, useful, and infrequently fallacious, so reviewing the whole lot is essential.
You’ve cited analysis suggesting a large development in AI roles by 2027. If a junior information scientist might solely be taught one engineering talent this yr to remain aggressive, what ought to or not it’s?
If I needed to choose one, it could be to discover ways to ship your work in a repeatable manner! Take one mission and make it one thing that may run reliably with out you babysitting it. As a result of in the true world, the perfect mannequin is ineffective if no one 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 current work has targeted closely on LLMs and time collection. Wanting forward into 2026, what’s the one rising AI subject 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). In addition to, if I do write a few “sizzling” subject, I would like 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 fascinating me so much.
I’m additionally getting extra interested by 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 together with her newest articles, you possibly can comply with her on TDS or LinkedIn.
