isn’t dying, however it’s evolving. Quick.
AI-related jobs are projected to develop ~40% year-over-year, creating over one million new roles by 2027.
On this article, I’ll take you thru my transition from Information Science to Ai Engineering, in addition to provide you with some sensible recommendation on methods to transition or to study extra about this space.
My path by Information Science to AI Engineering has been attention-grabbing and filled with learnings. Here’s a brief snapshot of my journey up to now:
- I graduated from Physics and Astrophysics (bachelor’s and grasp’s) and transitioned to Information Science;
- Carried out two internships overseas in Information Science and Machine Studying;
- Received my first full-time job as a Information Scientist within the greatest vitality firm of my nation;
- Transitioned to AI Engineering lower than a 12 months in the past (as of Might 2025) and now I work for an enormous logistics firm.
In case you are a information scientist, how usually do you concentrate on how your code reaches manufacturing? If the reply is ‘virtually by no means’, AI Engineering would possibly shock you.
Inquisitive about how real-world expertise in information science might form your journey into AI engineering, or what shocking challenges I confronted?
How is a day by day lifetime of an AI Engineer evaluate to a Information Scientist’s one?
What instruments and platforms I take advantage of now, in comparison with earlier than?
Maintain studying it to know all about it!
Whats up there!
My identify is Sara Nóbrega, and I’m a an AI Engineer.
I write about information science, Artificial Intelligence, and information science profession recommendation. Be sure to follow me to obtain updates when the following article is revealed!
Variations and Similarities Between Information Science and AI Engineering
AI Engineering is a really broad time period and it could even embrace many Data Science duties. Actually, it’s usually used as an umbrella time period.
As a Information Scientist, I as soon as spent 3 weeks tuning a mannequin offline. Now, as an AI Engineer, we now have 3 days to deploy it into manufacturing. Priorities shifted quick!
However does that imply that each roles are fully completely different and by no means overlap?
What if at some point you need to apply to an AI Engineer position? Are information science abilities transferable to the world of AI Engineering?
First, I’ll present you some findings of the analysis I did on this after which my private consumption and expertise on the topic.
I did some research for you…
From my investigation, the obligations of every position have broadened and converged over the previous three years.
Information Scientist job descriptions immediately embrace increasingly duties in addition to evaluation and mannequin tuning. They usually embrace: deploying fashions, constructing information pipelines, and making use of Machine Studying Operations (MLOps) finest practices.
Guess what, that is what I primarily do as an AI Engineer! (Extra on this within the subsequent sections).
For instance, a current Information Scientist posting I noticed explicitly required “expertise with enterprise DataOps, DevSecOps, and MLOps”.
Till some years in the past, information scientists centered primarily on analysis and modeling. Now, firms usually anticipate information scientists to be “full stack”, which overwhelmingly means, fluent in virtually all the things.
Which means that it’s anticipated that information scientists have some information of cloud platforms, software program engineering, and even DevOps, so their fashions can immediately assist merchandise.
One survey discovered 69% of information scientist job listings request machine studying abilities and about 19% point out NLP, up from simply 5% a 12 months prior.
Cloud computing abilities (AWS, Azure) and deep studying frameworks (TensorFlow/PyTorch) now seem in ~10–15% of information scientist advertisements as effectively, indicating a rising overlap with AI engineering talent units.
There’s a clear convergence within the talent units of Information Scientists and AI Engineers. Each roles closely use programming (particularly Python) and information abilities (SQL), and each want understanding of machine studying algorithms.
In line with an evaluation of 2024 job postings, Python is required in ~56–57% of each information scientist and ML engineer listings.
Cloud and MLOps abilities appear to be the new widespread floor, as AI Engineers are anticipated to deploy on AWS/Azure and likewise “cloud abilities might be important” for future information scientists.
The desk beneath highlights some core abilities and the way continuously they seem in job advertisements for every position, in response to the sources that I checklist within the references part:
At first look, the divergence is apparent. Information Scientist roles stay grounded in conventional information duties: Python, SQL, common machine studying, and deriving insights from structured information.
ML/AI Engineers are positioned a lot nearer to the world of software program engineering. These professionals are tasked with taking experimental fashions and making them strong, scalable, and constantly deliverable.
However there’s a clear convergence that’s attention-grabbing and strategic.
We will see that cloud platforms are more and more talked about for Information Scientists, and MLOps instruments are not confined to engineering roles. The talent units are mixing!
We’re seeing a development the place Information Scientists are being nudged nearer to the engineering stack.
My Private Journey and Consumption
What did I do as a Information Scientist? What do I do know as an AI Engineering?
To present you some context, I labored as an information scientist in an enormous vitality firm, the place my obligations revolved round growing time-series forecasting fashions (utilizing XGBoost, LightGBM, SARIMAX, and RNNs), producing and validating artificial information (by way of TimeGAN, statistical distributions, and imputation methods), doing deep and in depth statistical analyses and using machine studying fashions to deal with lacking information in huge information.
In case you are , I wrote a ton of useful articles to take care of time-series information.
A number of the instruments and platforms I used as a Information Scientist included: VSCode, Jupyter, MLflow, Flask, FastAPI, and Python libraries corresponding to TensorFlow, scikit-learn, pandas, NumPy, Matplotlib, Seaborn, ydata-synthetic, statsmodels, and others.
In my earlier internship, I’d use PyTorch, Transformers, Weights & Biases, Git, and Python libraries for information distillation, supervised studying, utilized statistics, pc imaginative and prescient, NLP, object detection, information augmentation, and deep studying.
The instruments and platforms I take advantage of now
Python remains to be the primary language I take advantage of. I do use Jupyter notebooks for prototyping, however most of my time is now spent writing Python code in VSCode (scripts, APIs, checks, and many others).
My work may be very linked to Microsoft Azure, significantly Azure Machine Studying, as my workforce makes use of it to handle, prepare, deploy, and monitor our ML fashions.
The whole MLOps lifecycle (from improvement all the way in which to deployment) runs in Azure. We additionally make the most of MLflow to trace experiments, evaluate completely different fashions and parameters and register all of the mannequin variations.
A significant shift for me from DS to AI Engineering has been the constant use of CI/CD instruments, particularly GitHub Actions. This was really certainly one of my first duties after I began this job!
GitHub Actions enable me to construct automated workflows that take a look at and deploy ML fashions, in order that they are often built-in into different pipelines.
Past machine studying, I additionally construct and deploy backend parts. For that, I work with REST APIs, with FastAPI and Azure Capabilities, to serve mannequin predictions and join them to our frontend purposes or exterior companies.
I’ve began working with Snowflake to discover and rework structured datasets utilizing SQL.
Relating to infrastructure as a code, I’ve used Terraform to handle cloud sources as code.
Different instruments I take advantage of embrace Git, Bash, and Linux atmosphere. These are vital for collaboration, scripting automation, troubleshooting, and managing deployments.
Some duties I’ve carried out as an AI Engineer
Now, I work as an AI Engineer for an enormous logistics firm.
The primary activity I used to be assigned to was to enhance and optimize steady integration/steady deployment (CI/CD) pipelines of ML fashions utilizing GitHub Actions and Azure Machine Studying.
What does this imply in apply, you ask?
Properly, my firm wished a reusable MLOps template that new initiatives might undertake with out ranging from scratch. This template is sort of a starter pack. It’s in a GitHub repo and has all the things you’d have to go from a prototype in a pocket book to one thing that may really run in manufacturing.
Inside this repo, there’s a Makefile (a script that allows you to run setup duties like putting in packages or working checks with a single command), a CI workflow written in YAML (a file that defines precisely what occurs each time somebody pushes new code, for instance, checks are run, and fashions get evaluated), and unit checks for each the Python scripts and the configuration information (to ensure all the things behaves as anticipated and nothing breaks with out us noticing).
In the event you want to study extra about this, I really wrote a full Dev Checklist for ML projects that describe these finest practices, and that’s completely beginner-friendly.
From linting and Makefiles to GitHub Actions and department safety, it’s filled with the sensible steps that I want I knew earlier:
👉 Read it here: From Notebook to Production — A Dev Checklist for ML Projects
Unit checks are literally a core a part of AI Engineering. They’re usually not the favourite activity of anybody… however they’re essential for ensuring issues don’t break when your mannequin hits the actual world.
As a result of think about you’ve spent days coaching a mannequin, solely to have a tiny bug in your preprocessing script mess all the things up in manufacturing. Unit checks assist catch these silent failures early!
However does this imply I’ve stopped performing Information Science duties? By no means!
Actually, certainly one of my present duties includes mapping departure and arrival instances, cleansing route information, and integrating the outcomes right into a frontend app.
I believe it’s a excellent instance of how Information Science (EDA, mapping, cleansing) blends with AI Engineering (integration, deployment consciousness).
I need to spotlight that each roles (Information Scientist and AI Engineer) may be fairly broad and their obligations usually differ from firm to firm, even sector to sector. What I’m sharing right here is simply based mostly on my private expertise, which can not replicate everybody’s journey or expectations in these roles!
Collaboration Patterns
One factor I’ve seen is that this overlap in obligations has pressured nearer collaboration with different workforce members. I’ve seen that information scientists are more and more working side-by-side with DevOps and backend engineers to make sure fashions really run in manufacturing.
A study found that 87% of machine studying options fail to make it out of the lab with out groups coordinating in an environment friendly method.
Over the past years, firms have acknowledged the necessity for collaboration. Actually, the necessity for MLOps finest practices have come to life to bridge this hole between information scientists and DevOps.
Largest Challenges So Far
I’m not gonna lie, this journey has been difficult. Everybody should pay attention to the imposter’s syndrome, and I’ve definitely suffered from it as effectively. I suppose it disappears over time as I really feel I add worth to the initiatives I take part in.
Proper after I began to work as an AI Engineer, the greatest problem was to get used to new instruments, and to make use of all of them collectively. As I used to be assigned an vital activity that solely I used to be engaged on (the MLOps template one), I felt I had immediately lots of duty. I needed to shortly study the YAML language, Github Actions and the way they hook up with Azure.
Since I used to be actually into MLOps, I ended up taking over the position of system architect in a number of initiatives. I used to be chargeable for determining how all of the items would match and work collectively, after which explaining it clearly to my managers.
I used to be undoubtedly not used to those obligations and roles, however over time I’ve grown extra assured in dealing with them.
Tricks to transition from DS to AI Engineering
I’d say that step one to change into an AI Engineer is to start out by being and curious about how the massive image of AI works. That is how I began.
That is how I began!
I began by asking myself: How will this mannequin really go stay to the customers? How will it add worth? How does the databases work, and the way can we fetch the info in manufacturing? How can I guarantee that in 6 months this mannequin nonetheless works? How can I guarantee that my mannequin might be as correct domestically as in manufacturing?
Then, I began studying articles on-line and LinkedIn posts as effectively, earlier than I transitioned to AI Engineering.
There’s a large quantity of helpful content material on-line, without spending a dime. I additionally began taking some on-line programs so my abilities change into extra stable.
In case you are in an information science position, you would ask your supervisor to start out contributing to manufacturing code in your workforce, or to incorporate you within the conferences with the AI Engineers. From my expertise, managers at all times like workers that need to study extra.
Then, you may study on-line about GitHub Actions, Docker, and Azure/AWS. Find out about vital manufacturing metrics like latency, uptime, monitoring
It is a very brief roadmap, I’ll go away the remainder of the guidelines for the following article 😉.
Ultimate Phrase
My Mindset Shifted: Why AI Engineers Should Suppose Like Devs
To transition to an AI Engineering position, you will need to take into consideration the huge image of a ML lifecycle: that’s, to ensure the mannequin will really work, create influence and add worth to the corporate.
What does this imply?
It means considering, throughout the entire lifecycle, how the mannequin might be built-in into real-world programs — how it is going to be deployed, monitored, scaled, and maintained over time.
It means considering past notebooks and coaching accuracy, and asking questions like: The place will this mannequin run? How will we replace it safely? What occurs if the enter information shifts subsequent month?
For these getting into or transitioning inside the AI house, bear in mind: you don’t have to grasp all the things, however you do want to know how your work suits into the bigger image of the ML lifecycle.
The deeper your empathy for the “different aspect” of the pipeline, the extra influence you’ll have.
As you seen by this text, transitioning to AI Engineering for me has been about working on, studying about and proudly owning your entire ML lifecycle, not simply the mannequin coaching.
In my previous position as an information scientist, I used to be performing conventional DS duties like EDA, anomaly detection, information wrangling, mannequin improvement and packaging. Certainly, it was immediately linked to what I had discovered in college.
As an AI Engineer, I really feel my day by day duties are a mix of each roles. I nonetheless discover and clear information, however I really feel I have to suppose like a dev, so I’m positive the fashions work in manufacturing and maintained over time.
Positively one of many greatest mindset shifts was studying methods to ship code prepared for manufacturing and likewise to develop a mindset of automation: automate installations, testing, deployment, monitoring.
It has been an attention-grabbing journey up to now, that I intend to doc and share additional on.
Thanks for studying! Hope you discovered this submit helpful.
🔔 Yet another factor!
I additionally write a free e-newsletter, Sara’s AI Automation Digest, the place I share month-to-month insights, instruments, and behind-the-scenes takes on AI, automation, and the way it’s remodeling the way in which we work.
Subscribe now and get entry to my FREE AI Tools Library — a curated Notion database of 20+ AI instruments with real-world use circumstances, options, and limitations.
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References
The Interview Query 2024 Data Science Report: The Rise of AI Jobs (Updated in 2024)
MLOps: Connecting Data Scientists and DevOps Teams
The Future of Data Science: Job Market Trends 2025–365 Data Science
