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Key Takeaways
- AI engineers and software engineers serve different business needs—AI engineers focus on data and prediction, while software engineers build user-facing systems.
- AI engineers are ideal for businesses with strong data infrastructure and automation goals, while software engineers are best for product and platform development.
- Hiring costs vary widely, with many companies reducing expenses by offshoring projects to skilled talent in regions like Latin America.
If artificial intelligence (AI) and software are both built on code, shouldn’t one engineer be able to handle both? It’s a common assumption that often leads to the wrong hire.
The truth is that artificial intelligence engineers and software engineers bring very different skill sets to the table. While both work in technical environments and use programming languages, their goals, tools, and business impact aren’t the same. Confusing the two can slow down your project, waste your budget, and steer your product in the wrong direction.
In this guide, we’ll break down the difference between AI engineers and software engineers in plain terms. You’ll get a clear look at what each role does, how they compare in costs and responsibilities, and how to decide which one your business actually needs or whether you might need both.
Let’s clear up the confusion and help you make the right hire.
What Does an AI Engineer Do?
AI engineers turn data into decisions. Instead of building traditional software products, they create systems that can learn, predict, and adapt over time.
In a business context, it means things like chatbots that understand customer questions, fraud detection tools that flag suspicious behavior, and recommendation engines that drive sales by personalizing the user experience.
At the core of this work is a mix of machine learning, natural language processing, and predictive analytics. AI engineers train models, fine-tune algorithms, and deploy AI systems that evolve based on new data. These aren’t one-time builds—they’re systems that grow smarter the more they’re used.
The typical AI toolkit includes Python, TensorFlow, PyTorch, and plenty of math. It’s a highly technical role but one that can unlock huge value when used the right way.
With the explosion of interest in AI, some of the world’s biggest companies are racing to hire top AI talent. In fact, last year alone, there were over 2.9 million Google searches for jobs in AI-related fields. It's a clear sign that demand is only growing.
If your business is sitting on large amounts of data or needs to automate complex decisions, hiring an AI engineer might be the next best decision for your business.
What Is the Role of a Software Engineer?
The introduction of AI has been heralded as the end of the need for the software engineer, but that’s far from reality. Because software engineering revolves so closely around user needs, businesses still need experts to build functional, reliable systems tailored to real-world use.
Whether you're launching a new mobile app or streamlining internal workflows, software engineers are the ones writing the code that makes it happen.
Their responsibilities span across coding, debugging, testing, and designing system architecture. They make sure everything works, scales, and performs well under real-world conditions. Typical projects include customer-facing platforms, mobile applications, internal dashboards, and infrastructure tools that support other teams.
The tools of the trade vary depending on the project, but you’ll often see JavaScript, Java, C++, SQL, React, and Node.js in their stack.
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AI Engineer vs. Software Engineer: What Sets Them Apart?
When you're hiring for a technical role, the difference between an AI engineer and a software engineer might not be obvious at first glance. Both write code. Both work in highly specialized environments.
What they build and how they contribute to your business can be very different, though. Knowing the distinction helps make sure that you hire for the right outcome and avoid wasting time or budget.
Core focus
AI engineers focus on data, not just code. Their job is to create systems that can learn, adapt, and improve over time. These systems are built to analyze patterns, make predictions, or automate complex decision-making processes. Software engineers, by contrast, focus on building functional systems and applications that users interact with directly.
For example, an AI engineer might help a logistics company predict delivery delays based on real-time traffic and weather data. A software engineer would build the dashboard that shows that prediction to the operations team and lets them reroute drivers if needed.
Typical tasks
The daily work of an AI engineer often involves training machine learning models, tuning algorithms, working with large datasets, and deploying intelligent systems that respond dynamically to inputs. A retail company might rely on AI engineers to build a recommendation engine that shows shoppers the most relevant products based on browsing history and behavior.
Software engineers are responsible for designing the system architecture, writing and maintaining code, testing features, and fixing bugs. If the same retail company needs a mobile app for shoppers to browse products, track orders, and complete purchases, the software engineer builds it.
Tech stack and skills
AI engineers tend to work with tools like Python, TensorFlow, Keras, and PyTorch. Their skills often include deep knowledge of statistics, data science, and machine learning principles. They are usually comfortable working with both structured and unstructured data and running experiments to improve model accuracy.
Software engineers rely on languages and frameworks such as JavaScript, Java, C++, SQL, React, and .NET. Their work requires strong problem-solving skills and a deep understanding of system design, security, and performance optimization.
Cost of hiring
When comparing talent costs, there's a significant difference between software engineers and AI engineers. The cost of hiring an AI engineer tends to be higher. US salaries for AI engineering roles often exceed $150,000 and can reach well into the $300,000 range for senior talent in cities like San Francisco.
Many companies are looking globally to find the right talent and control costs. This strategic approach to finding talent allows companies to stretch their technology budgets further while still accessing the specialized skills needed for innovative AI and software development projects.
For example, US businesses can achieve cost efficiencies while maintaining excellent quality and similar time zone advantages by offshoring these roles to Latin America.
Here’s a quick comparison of US salaries to those in Latin America (LatAm):
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Looking to the best countries to hire offshore AI engineers and software engineers gives businesses more flexibility.
For companies exploring this route, it helps to understand the advantages of nearshore AI developers and software engineers when planning your projects.
How to Choose the Right Engineer for Your Business
Choosing between an AI engineer and a software engineer starts with understanding your business needs. Each role brings unique strengths, so the right fit depends on where you're headed and what you’re building.
Start with your project goals. If you're looking to develop smart systems that analyze data, predict outcomes, or make decisions without human input, you’ll want an AI engineer. If your priority is to build an application, platform, or internal tool, then a software engineer is usually the better choice.
Next, consider your data readiness. AI engineers rely on high-quality data to deliver results. If your business doesn’t yet have the data infrastructure and analysis in place, AI might be premature, and hiring a data analyst or data scientist first might be necessary.
Budget also plays a role. AI engineers tend to cost more, not just in salary but also in the tools and computing power required to support their work. For many early-stage businesses, starting with software engineers provides more flexibility and faster output.
Your team structure matters too. If you’re building a new product from scratch, software engineers are often the first hires. But if you’re expanding an existing team or adding advanced capabilities, AI engineers can help automate and optimize key processes.
In some cases, a blended team is the best approach. This is especially relevant for businesses developing AI products that still need stable software architecture around them.
Final Thoughts
AI engineers and software engineers aren’t the same. Each serves a different purpose and brings unique value to your business.
Before making a decision, think about what your project really needs. If you’re building something interactive, like an app or a platform, a software engineer is the better fit. If your primary goal is to automate decisions or make sense of large datasets, then an AI engineer is the way to go.
Near helps you make smarter hiring choices by connecting you with skilled, cost-effective AI engineers and software engineers in Latin America, helping you get access to talent that fits both your goals and your budget.
To learn more about hiring AI engineers, see our article “Need to Hire an AI Engineer? Here’s What You Need to Know.” It breaks down what you need to know about finding a skilled AI engineer—from hiring strategies to skills to look for.
And for guidance on hiring software engineers, have a look at our guide to the top 13 staffing agencies that can find you top talent.
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