Within the Writer Highlight sequence, TDS Editors chat with members of our group 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 sequence, 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: methods to keep calm after I don’t know what’s occurring, and 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 in the event you can’t clarify your pondering or reproduce your outcomes. That mindset might be essentially 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 day by 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 method survive actual life?” Being an AI Engineer isn’t a lot concerning the good reply however extra about constructing one thing reliable. And actually, that change was uncomfortable at first… but it surely 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 pace?
If now we 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 method to monitor what occurs after launch.
I additionally like transport in small steps. As an alternative of pondering “deploy the ultimate factor,” I feel “deploy the smallest model that creates worth with out inflicting chaos.”
How do you suppose we might use LLMs to bridge the hole between knowledge scientists and DevOps? Are you able to share an instance the place this labored effectively for you?
Information scientists converse in experiments and outcomes whereas DevOps people converse in reliability and repeatability. I feel LLMs may help as a translator in a sensible method. As an illustration, to generate assessments 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 hastens the method so much and retains the motivation ongoing. I feel the secret is to deal with the LLM as a junior who’s quick, useful, and sometimes improper, so reviewing the whole lot is essential.
You’ve cited analysis suggesting an enormous development in AI roles by 2027. If a junior knowledge scientist might solely be taught one engineering talent this 12 months to remain aggressive, what ought to it’s?
If I needed to decide one, it might be to learn to ship your work in a repeatable method! 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 very best 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 latest work has targeted closely on LLMs and time sequence. Trying forward into 2026, what’s the one rising AI subject that you’re most excited to put in writing about subsequent?
I’m leaning an increasing number of towards writing about sensible AI workflows (the way you go from an thought to one thing dependable). In addition to, if I do write a couple of “sizzling” subject, I need it to be helpful, not simply thrilling. I wish to write about what works, what breaks… The world of information science and AI is stuffed with tradeoffs and ambiguity, and that has been fascinating me so much.
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 together with her newest articles, you’ll be able to observe her on TDS or LinkedIn.
