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Key Takeaways
- AI engineers develop intelligent systems that automate tasks and make decisions, while machine learning engineers create models that learn from data.
- Businesses should hire AI engineers for automation and reasoning and ML engineers for predictive analytics and data-driven optimization.
- Due to high demand, companies can outsource their projects, finding top AI and ML talent from offshore and nearshore locations.
These days, it seems artificial intelligence (AI) and machine learning (ML) are everywhere. From recommendation engines and AI-powered security systems to fraud detection and self-driving cars, everyone’s talking about them, and adoption rates are only increasing.
When it comes to hiring AI and ML talent for your business, the AI engineer versus ML engineer debate can get confusing. Aren’t they basically the same thing? Well, not quite.
While both roles deal with intelligent systems, their responsibilities and expertise are different. Hiring the wrong specialist could slow your project down or lead to expensive misalignment.
This guide breaks down the key differences between AI and ML engineers, their roles, and where to find the right talent for your project. Whether you need an expert in predictive modeling or someone to build autonomous systems, you’ll have a clear answer by the end of this article.
What Does an AI Engineer Do?
Artificial intelligence engineers design and build intelligent systems that automate tasks, analyze data, and even mimic human decision-making. If you’ve ever interacted with a chatbot or wondered how self-driving cars avoid collisions, you’ve seen their work in action.
Their day-to-day involves:
- Building AI models that power chatbots, fraud detection, and automation tools.
- Programming in Python, Java, and C++ to create scalable and effective AI solutions.
- Using deep learning, computer vision, and natural language processing (NLP) to enhance system intelligence.
Real-world applications
AI engineers are behind some of the most impactful advancements today:
- AI-powered chatbots that handle customer service without human involvement.
- Fraud detection systems that scan thousands of transactions in real time.
- Autonomous vehicles that analyze sensor data to navigate safely.
Forbes predicts that small and midsize businesses will be transformed by AI this year, meaning demand for AI engineers is only going up.
What Does an ML Engineer Do?
Machine learning engineers build and optimize models that allow systems to learn from data without needing explicit programming for every outcome. If you've ever received a Netflix recommendation that was eerily spot-on, that’s an ML engineer’s work in action.
Their job is all about using their engineering skills to teach machines to recognize patterns and make predictions. That involves:
- Building predictive models using structured and unstructured data.
- Designing self-improving algorithms that refine over time, such as recommendation engines.
- Managing data pipelines and cloud-based machine learning platforms for scalability.
Real-world applications
ML engineers power many of today’s most advanced technologies:
- E-commerce recommendation systems (e.g., Amazon, Netflix, Spotify).
- AI-driven healthcare diagnostics that detect diseases faster than traditional methods.
- Risk assessment models that help banks and insurers predict fraud and creditworthiness.
Finance, healthcare, and telecommunications, especially, are driving the demand for ML engineers as companies race to leverage data for better decision-making. Companies looking to enhance automation and analytics will benefit from hiring ML talent.
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Key Differences Between AI and ML Engineers
AI and ML engineers both work with intelligent systems, but they focus on different aspects of development. The simplest way to put it is that all ML engineers work in AI, but not all AI engineers work in ML.
How are they different?
The key distinction lies in what they build:
- AI engineers create systems that mimic human intelligence, such as chatbots, robotics, and automation tools that simulate human-like thinking.
- ML engineers build models that learn from data, fine-tuning predictions and improving performance over time.
Their focus also differs:
- AI engineers develop broader AI applications, like computer vision, NLP, and robotics.
- ML engineers specialize in predictive modeling, powering fraud detection, personalized product recommendations, and business forecasting.
- AI engineers often use deep learning and symbolic AI, while ML engineers rely more on statistical models and algorithm optimization.
Where do their skills overlap?
Despite these differences, both AI and ML engineers need:
- Strong programming skills (Python, Java, or C++)
- Expertise in AI frameworks like TensorFlow and PyTorch
- A solid foundation in and deep understanding of data science and analytics
Who should you hire?
Choosing between an AI engineer and an ML engineer depends on your project’s goals.
- If you need a system that makes decisions on its own, go for an AI engineer.
- If your focus is data-driven predictions and optimization, an ML engineer is the better fit.
When do you need both?
Some businesses require both AI and ML engineers to develop complex, intelligent systems.
For example:
- A self-driving car needs an AI engineer to design its decision-making framework (steering, braking, navigation) and an ML engineer to improve object detection and driving predictions based on real-world data.
- A voice assistant like Alexa or Siri relies on AI engineers to handle NLP and decision-making, while ML engineers refine speech recognition and response accuracy over time.
- A financial analytics platform may need an AI engineer to build a system that automates reporting and an ML engineer to enhance risk assessment models that improve with more data.
As businesses increasingly rely on AI-driven solutions, demand for AI and ML engineers continues to grow.
Where to Find AI and ML Engineers
Let's talk about where you can actually find AI and ML engineers. With demand for these specialists growing every day, the question isn't just who to hire but where to look.
Here are your main options:
Freelance platforms
Sites like Upwork, Toptal, and Fiverr connect you with independent AI and ML engineers for project-based work. This works well when you need specialized help for a one-off project or want to test a concept before committing to a full-time hire.
Just be aware that quality varies dramatically, and you'll need solid technical knowledge to evaluate candidates effectively.
Job boards
Traditional routes like LinkedIn, Indeed, and specialized tech boards can help you find full-time AI and ML talent.
The challenge here is standing out among hundreds of companies also hunting for the same skills. Unless you're a known tech brand or offering top-of-market compensation, you might spend months trying to find the right person.
Staffing and recruitment agencies (US-based)
Recruitment and staffing agencies pre-screen AI/ML engineering candidates and match them with your needs, saving you time on sourcing and vetting talent.
Specialist staffing and recruitment for global talent
With AI and ML roles being fully remote-friendly, many businesses hire outside the US to access top talent at a lower cost. Many US-based businesses hire nearshore engineers within Latin American countries like Brazil, Argentina, and Mexico for the additional benefits of time zone and cultural alignment.
These specialist offshore hiring recruitment companies understand both the technical requirements and the practical aspects of international hiring, handling everything from sourcing and technical assessment to communication evaluation and cultural fit.
Final Thoughts
Hiring AI and ML engineers can be challenging, especially when each role serves a different purpose. AI engineers focus on building systems that make decisions, while ML engineers develop models that learn from data. The right choice depends on whether you need automation and reasoning or data-driven predictions.
These roles are in high demand, and hiring within the US is costly. So, many companies are turning to offshore or nearshore AI and ML engineers, especially in Latin America, to access top talent while keeping expenses under control.
That’s where Near can help. We source highly skilled professionals in the same time zones as US businesses, so collaboration runs smoothly and your projects move forward without delays.
To learn more about hiring skilled remote engineers, explore our guides on how to hire a remote machine learning engineer or a remote AI engineer.
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