Artificial intelligence isn’t just a buzzword anymore—it’s become the heartbeat of modern innovation. From personalized shopping experiences to predictive maintenance in manufacturing, AI and machine learning (ML) are quietly powering the technologies we rely on daily. And in 2025, more businesses than ever are tapping into this potential to stay competitive, automate smarter, and unlock entirely new revenue streams.
But as companies dive into the AI-driven future, one big question keeps popping up: Should we hire an AI engineer or a machine learning engineer? Spoiler alert—they’re not the same thing.
While both roles sound futuristic and often overlap, they focus on different parts of the AI ecosystem. Choosing the right one can mean the difference between building a product that thinks and creating a product that learns. If you're unsure what kind of expert your team needs, you’re in good company—many decision-makers are navigating the same challenge.
This article will break down what each role really involves, how they differ, and most importantly, which one makes sense for your business goals in 2025. Whether you’re launching your first AI product or scaling up an existing one, we’ll help you make the right hiring call, minus the tech jargon and confusion.
What Does an AI Engineer Do?
An AI engineer is like the architect of intelligent systems—someone who builds machines that can think, make decisions, and solve problems (sometimes better than we do). These engineers design and implement algorithms that allow computers to simulate human behavior, whether it’s recommending your next binge-worthy show or enabling a self-driving car to recognize a stop sign.
But AI engineers don’t just write fancy code—they bridge the gap between cutting-edge research and real-world business applications. Their job is to take theoretical models and transform them into scalable products that can operate in complex environments.
Typical Responsibilities:
- Designing AI models to perform reasoning, decision-making, or pattern recognition
- Integrating AI capabilities into apps, platforms, or devices
- Collaborating with product teams to align AI features with user needs
- Working with large datasets to feed AI algorithms
- Monitoring model performance and improving accuracy over time
Key Tools and Technologies:
- Languages: Python, Java, C++
- Frameworks: TensorFlow, Keras, PyTorch
- Concepts: Natural language processing (NLP), neural networks, deep learning, computer vision
- Platforms: AWS AI services, Google Cloud AI, Azure Cognitive Services
Where You’ll Find Them:
AI engineers are in demand across nearly every sector, from healthcare and fintech to robotics and retail. If your company is looking to develop products with autonomous capabilities or human-like decision-making, this is the expert to call.
Think of them as the creators of the “brains” behind your product. If your vision involves AI that mimics human behavior—chatbots that feel natural, recommendation engines that get smarter over time, or systems that adapt in real time—then you’re probably in AI engineer territory.
What Does a Machine Learning Engineer Do?
If an AI engineer builds the brain, a machine learning engineer is the one who trains it to learn from experience. These professionals specialize in designing algorithms that allow systems to improve automatically through data, with no manual programming required after the initial setup. It's like teaching a machine to get better at its job the more it does it.
Machine learning engineers are deeply embedded in the data world. They spend their days developing models that can detect fraud, forecast demand, personalize recommendations, and more—all by identifying patterns in massive amounts of information.
Typical Responsibilities:
- Creating and training machine learning models using structured and unstructured data
- Cleaning, processing, and organizing datasets for optimal performance
- Testing and fine-tuning model parameters to boost accuracy
- Deploying models into production environments and monitoring their performance
- Working with data scientists to translate insights into scalable solutions
Key Tools and Technologies:
- Languages: Python, R, SQL, Scala
- Libraries/Frameworks: scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch
- Concepts: Supervised and unsupervised learning, model evaluation metrics, feature engineering
- Platforms: Databricks, AWS SageMaker, Google Cloud Vertex AI
Where You’ll Find Them:
Machine learning engineers thrive in data-rich environments—think fintech, ad tech, health tech, e-commerce, and any other field where analytics and predictions are critical to success.
If your company has tons of historical data and you're looking to make data-driven predictions, like identifying churn risks, optimizing logistics, or scoring leads, then a machine learning engineer is probably the one you need. They’re the data whisperers who turn raw numbers into real-time, actionable intelligence.
AI Engineer vs. Machine Learning Engineer: Side-by-Side Comparison
While both roles contribute to the broader field of artificial intelligence, they play very different parts in your tech strategy. Understanding these differences is essential when building a team, launching an AI product, or scaling your data operations. Below is a side-by-side comparison to help you grasp the distinct focus, tools, and collaboration patterns of each role.
Use this chart as a quick reference to align the right expertise with your specific business needs:
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Key Takeaways:
- AI engineers are system-level thinkers. They’re the go-to professionals when you're building products that need to mimic human intelligence, such as autonomous vehicles, virtual assistants, or smart robotics. Their work often blends deep learning with user experience design, making them ideal for full-stack AI development.
- Machine learning engineers, on the other hand, specialize in making data work smarter. They live in a world of training data, model accuracy, and algorithmic efficiency. If your business is sitting on a goldmine of data and wants to unlock insights or make predictions, ML engineers are your data-driven superheroes.
The right choice depends on whether you’re solving problems that require learning from data (ML engineer) or building solutions that require intelligent behavior and decision-making (AI engineer).
By mapping out your product goals and technical challenges, you can more confidently decide which role to hire, or if your team needs both to fully realize your vision.
Which One Does Your Business Need?
When it comes to choosing between an AI engineer and a machine learning engineer, the answer isn’t one-size-fits-all—it depends entirely on what you’re building and where your business is headed. Below are a few guiding questions and scenarios to help you figure it out:
Ask Yourself:
- Are we trying to build a system that mimics human thinking (e.g., decision-making, problem-solving, speech recognition)?
- Do we need to predict outcomes or uncover insights from large datasets?
- Is this a new AI initiative, or are we optimizing and scaling something already in place?
- Do we have a strong data infrastructure or need someone who can build from scratch?
When to Hire an AI Engineer:
- You’re developing products that require intelligent automation, such as a virtual assistant, a vision-based recognition system, or autonomous software.
- You need someone to design full AI systems, integrating complex logic with user-facing interfaces or hardware.
- Your focus is on building new features that rely on real-time decision-making.
Example: A SaaS company building a smart CRM to interpret customer messages and trigger automated workflows.
When to Hire a Machine Learning Engineer:
- You have access to lots of data and want to use it to predict behavior, personalize experiences, or improve decision-making.
- You want to optimize an existing product with ML capabilities (e.g., recommendation engine, fraud detection).
- Your data team needs an expert to build production-ready ML models and work with infrastructure at scale.
Example: An e-commerce platform that wants to recommend products based on purchase history and browsing patterns.
What If You Need Both?
Businesses often benefit from hiring both roles, especially if you’re scaling a complex AI product. AI engineers can handle the architecture and integration, while ML engineers fine-tune the learning models that power the intelligence behind it.
If your project is still early-stage, you might even consider hiring a hybrid profile: someone with broad AI/ML experience who can wear multiple hats before you scale into more specialized roles.
Hiring Considerations in 2025
Hiring an AI or machine learning engineer in 2025 isn’t just about finding the right technical skill set—it’s also about navigating a competitive talent market, aligning with your business goals, and thinking strategically about team structure. Here's what to keep in mind:
Talent Availability & Competition
AI and ML engineers are among the most sought-after professionals today. With startups, tech giants, and even traditional industries all competing for the same experts, finding top-tier talent can be both time-consuming and expensive. Expect high salaries, long hiring cycles, and candidates interviewing with multiple companies simultaneously.
Tip: Move fast when you find the right fit—and be prepared to sell your company’s mission just as much as you're evaluating the candidate.
In-House vs. Outsourced Talent
If building an internal AI team from scratch seems daunting, you’re not alone. Many companies are turning to outsourcing or nearshoring models to access top-tier talent without the overhead. Nearshoring to Latin America, for instance, offers:
- Cost savings of up to 60% compared to U.S.-based hires
- Access to highly skilled, English-speaking engineers
- Real-time collaboration across compatible time zones
Tip: Consider partnering with a specialized recruitment agency like South to find pre-vetted AI and ML engineers in Latin America.
Focus on Business Alignment, Not Just Code
Technical expertise is a given, but what sets great AI/ML talent apart is their ability to understand your product, users, and objectives. Look for engineers who ask questions about your goals and can translate those into scalable, intelligent systems.
Tip: During interviews, present real-world scenarios from your product roadmap and ask how they’d approach the problem.
Don’t Overlook Soft Skills
While it’s easy to focus on algorithms and frameworks, collaboration is critical, especially when these roles interact with product managers, designers, and data analysts. Communication, curiosity, and adaptability are all essential qualities in a high-performing AI/ML hire.
Future-Proofing Your Team
AI and machine learning move fast. Look for candidates passionate about continuous learning and comfortable experimenting with emerging tools. You’re not just hiring for today—you’re building for what’s next.
The Takeaway
AI and machine learning are no longer reserved for cutting-edge startups—they’re becoming standard drivers of growth, automation, and innovation across nearly every industry. But success doesn’t come from jumping on the trend—it comes from hiring the right people to lead it.
Whether you need an AI engineer to develop intelligent systems or a machine learning engineer to unlock insights from your data, the key is clarity: know what you’re building, understand the strengths of each role, and hire accordingly.
And if finding the perfect tech talent feels overwhelming, you don’t have to do it alone.
At South, we specialize in connecting U.S. companies with pre-vetted AI and ML engineers from Latin America—professionals who bring world-class skills, strong communication, and real-time collaboration to your projects.
If you’re ready to find the right engineer for your team, schedule a free call with us today and let’s turn your AI vision into reality!
Frequently Asked Questions (FAQs)
What’s the main difference between an AI engineer and a machine learning engineer?
An AI engineer focuses on building systems that simulate human intelligence, while a machine learning engineer builds models that learn from data to make predictions or decisions.
Can one person do both roles?
In early-stage startups or small teams, you might find hybrid profiles. But for complex projects, hiring specialists ensures deeper expertise and better results.
What are the salary expectations for AI and ML engineers in 2025?
Salaries vary by location and experience, but AI engineers often earn slightly more due to the broader scope of their role. Outsourcing or nearshoring can reduce costs significantly.
How do I know which role is right for my business?
If your goal is to build intelligent systems with real-time decision-making, go for an AI engineer. If you need to analyze data and make predictions, a machine learning engineer is your best bet.
Is it better to hire in-house or outsource AI/ML roles?
That depends on your resources and goals. In-house gives you full control, while outsourcing (especially to Latin America) offers cost-efficiency, flexibility, and faster hiring times.
