Artificial intelligence is a remarkable technology that has the potential to transform nearly every industry. However, the big mistake that many businesses are making is that they’re adopting AI systems randomly, without thinking about what systems will deliver real results.
Many organizations have access to large datasets and powerful AI models but struggle to turn these assets into software that solves real business problems. AI engineers fill this gap. Unlike researchers who develop algorithms and improve models, AI engineers focus on building and integrating artificial intelligence into everyday business applications with clear use cases.
Whether you’re a software developer looking to specialize or a professional from an unrelated field, this article gives you a roadmap for how to become an AI engineer.
What Is an AI Engineer?
An AI engineer builds software and infrastructure that puts large language models to practical use. This means constructing data pipelines, developing APIs that connect models to real products, and managing the systems required to train and run those models at scale.
AI engineering sits at the intersection of software engineering and machine learning. AI engineers typically work with languages like Python and frameworks such as TensorFlow or PyTorch. In addition, they often use cloud platforms and containerization tools to deploy and maintain production systems.
The output of this work is tangible. Some things that an AI engineer may build include:
- Recommendation engines that keeps shoppers engaged
- Fraud detection systems that flags suspicious transactions in real time
- Natural language interfaces that streamline customer support
Common AI Engineer Titles and Career Path
Most AI engineers enter the field through adjacent roles where they build core skills in coding, data pipelines, and model deployment. Entry-level titles include junior software engineer, data analyst, or machine learning assistant.
AI professionals then move into mid-level positions that take ownership of AI-driven features from end to end. AI engineer and applied ML engineer are common titles at this career stage.
Finally, senior professionals such as AI architects and intelligent systems developers set technical direction, align AI strategy with business objectives, and scale AI capabilities across teams.
| Level | Example Roles | Scope of Responsibility | Earning Potential* |
|---|---|---|---|
| Entry-level |
| Supporting AI developers with specific model-building tasks; deploying intelligent or automated systems | $102,800 |
| Mid-level |
| Building intelligent systems, deploying them, and integrating them with other business tools | $185,200 |
| Senior-level |
| Aligning AI strategy with business objectives; scaling AI across teams; defining technical direction and priority | $200,400 |
*Average salary potential calculated from the median annual salary of listed roles. Salary data pulled from Lightcast.
The Roadmap to Becoming an AI Engineer
AI engineering requires a broad base of interconnected technical skills. In addition to building a foundation in coding and data, you must also understand the theory behind machine learning and then gain practical skills in model deployment.
For this reason, aspiring AI engineers should consider earning master’s in computer science, which provides a crucial technical background in software engineering.
Step 1: Master the Foundations
Like any form of software engineering, coding is the foundation of AI engineering. Though professional AI engineers can leverage a variety of programming languages, Python is an ideal starting point because it dominates machine learning development and offers a vast ecosystem of data-focused libraries.
Equally important is data handling. Before you start to model, you must be able to gather, clean, and manipulate datasets using tools like NumPy and Pandas.
The online MS in Computer Science from Pace University is designed to support professionals who need to build these skills. Students will gain foundational coding expertise, then explore courses like Python Programming and Database Management Systems to get into advanced applications.
Step 2: Understand Algorithms and Theory
Algorithms provide the instructions that enable machines to process data, identify patterns, and make predictions efficiently. Understanding how they work helps AI engineers manage and troubleshoot the systems they design. For example, knowing how a sorting algorithm manages speed with memory will inform your decisions about how to process large datasets.
It’s also essential to understand the basics of linear algebra, calculus, probability, and statistics. A grounding in these advanced forms of mathematics help AI engineers understand the full picture of machine learning and leverage what it can offer. Someone with a strong grasp on matrix operations, for instance, will find neural networks intuitive rather than mysterious.
Step 3: Specialize in Intelligent Systems
To transition from traditional software development into AI system development requires specialized knowledge. While this can be done through on-the-job experience, it’s generally more streamlined to pursue a computer science education where you can specialize in Artificial Intelligence.
A master’s degree allows you to progress from general programming and theory to applied work in AI and machine learning. At the specialization stage, students should prioritize independent practice, neural networks, natural language models, and computer vision models.
Your goal is to learn how to build AI models and train them for specific tasks while handling real-world data complexities.
Intro to Deep Learning: Moving Beyond Basic Automation to Neural Network
Deep learning moves AI beyond simple automation. Unlike traditional machine learning, which often requires manual feature selection, deep learning uses neural networks to learn patterns directly from large unstructured datasets and handle complex tasks. This allows it to power technologies such as:
- Realistic voice assistants
- Automated diagnostics for medical engineering
- Recommendations on streaming platforms or social media
- Fraud detection for suspicious account activity
- Facial recognition for pictures, videos, and device log-in
At Pace, the online MS in Computer Science AI concentration includes an Intro to Deep Learning course. Students gain hands-on experience and learn key considerations for deploying these models effectively.
Bridging the Gap: Projects and Portfolios
For aspiring AI engineers, a portfolio of hands-on projects is indispensable because it demonstrates that you not only understand these concepts but have the ability to apply them to real problems. Graduate programs in computer science tend to emphasize hands-on application, meaning that you will complete projects that you can save and share with prospective employers in the future.
The most effective portfolio projects mirror real business use cases, but collaborative projects can add another dimension to your portfolio. Working within a team, shipping something under real constraints, and navigating existing codebases are experiences that independent projects can’t fully replicate.
Why Transition to a Career in AI?
AI tools are already becoming core infrastructure. Financial institutions leverage it for fraud detection and risk modeling, while retailers, logistics companies, and manufacturers integrate AI throughout operations for built-in efficiency and automation.
This means employers need engineers who can build and maintain bespoke AI systems for business use. However, demand for skilled developers and engineers continues to outpace supply across industries.
This gap creates an opportunity for professionals with an interest in applied machine learning. In addition to competitive salaries, AI engineering roles offer experience with complex systems and opportunities to specialize in areas like natural language processing, computer vision, or predictive analytics.
Frequently Asked Questions
About the Online Master of Science in Computer Science at Pace University
The Pace University online Master of Science in Computer Science provides hands-on training that professionals need to excel in leadership or managerial positions in computer science and systems, or to transition into the field. Select a focus area in artificial intelligence or application development, or choose to create your own with our wide variety of electives. The master’s degree program is taught by experienced practitioners and active members of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers, Inc. (IEEE).
Learn more today by requesting a brochure, or get started on your application.
Pace University also offers an on-campus option for the MS in Computer Science.
Request
Information
To learn more about the online Master of Science in Computer Science program, fill out the fields in this form to download a free brochure. If you have any questions at any time, please contact an enrollment specialist at (914) 758-1080.