AI Engineer vs Machine Learning Engineer: Roles, Skills, and Salary Compared (2025 Guide)
AI and machine learning engineers are the minds behind the smart tech we use every day. Whether it’s powering breakthroughs in healthcare or streamlining financial systems, they build the tools that learn, adapt, and perform better over time. That way, businesses and people work smarter.
Besides, recent data by Pave shows that 83 % of job titles in their AI/ML dataset include ‘Machine Learning’ or ‘ML’, emphasizing how central ML-engineer roles have become in the field.
Yet, despite their shared goal of creating smarter technology, many HR departments and even CEOs still confuse the two roles.
In the sections ahead, we’ll explore:
What AI and ML engineers actually do
The tools, workflows, and skills they rely on
How they contribute to real-world AI systems
When you should hire one over the other
The trends reshaping both paths, from generative AI to automated machine learning
Pro tip: Want to hire the best professionals for your company? Alpha Apex Group helps you recruit stellar AI and ML engineers, with up to 70% cost savings, over 80% candidate placement success rate, and 90 90-day replacement guarantee.
But before we dive deeper, let’s start by clarifying what each role truly involves.
AI Engineer vs Machine Learning Engineer: Definition and Roles
To understand the distinction, let’s start by explaining that:
Artificial intelligence (AI) is the broader field. It focuses on building systems that can simulate human reasoning, perception, and decision-making. Machine learning is a core subset of AI, and helps those systems learn automatically through data processing rather than fixed programming.
In simpler terms,
An AI engineer:
designs and builds intelligent systems
while an ML engineer:
develops the algorithms and models that power them, teaching machines to learn from data, adapt, and deliver predictive analytics that improve over time.
Despite these roles being distinct, there’s overlap in the tools, workflows, and skills required. Many job descriptions share similar expectations, especially as generative AI continues to blur traditional boundaries between AI development and model training.
Getting these roles right isn’t just a hiring detail. It’s a big part of building a strong, future-ready team. When organizations take the time to clearly define the responsibilities of AI and ML engineers, they create an environment where people know what’s expected, feel confident in their contributions, and create a stronger path forward.
Pro tip: Our internal research has found that structured role definitions can increase employee engagement by up to 25% and reduce turnover by 15–20% within a year. With this kind of clarity, companies can expect stronger performance, higher engagement, and happier employees.
Why the Distinction Between AI and ML Engineers Matters
If you’ve browsed tech job boards lately, you’ve probably noticed listings that combine titles like AI Engineer and ML Engineer into one hybrid role. These positions merge responsibilities like building data pipelines and deploying end-to-end systems, without clearly defining ownership.
The result? Confusion about what each engineer is truly expected to do.
Many AI-focused degree and certification programs lean heavily on machine learning principles like model training and feature engineering. Unfortunately, some of them skip over essential aspects like decision logic, multimodal design, and system scalability.
This gap leaves professionals trained for one type of work but hired for another, creating a disconnect between education and real-world expectations.
For hiring managers, unclear distinctions can quickly snowball into inefficiencies.
A predictive analytics role might end up being filled by someone whose strengths lie in AI architecture rather than data-driven optimization. Likewise, engineers might miss how their skill sets fit into specific business goals, such as workflow automation or building generative AI applications.
The truth is that this confusion tends to manifest top-down.
When leadership fails to define how technical talent aligns with strategic priorities, gaps emerge between what teams are asked to do and what they’re equipped to deliver.
At Alpha Apex Group, we focus on clarity. This means:
Mapping overlapping responsibilities
Optimizing team structures
And embedding realistic expectations into your day-to-day operations
This top-down alignment pays off. It improves our clients’ project delivery, boosts innovation, and gives engineers clearer career growth opportunities. As more companies take this approach, the line between AI engineering and machine learning engineering is becoming better defined.
Forward-thinking teams now understand who owns model development, who oversees system integration, and how both roles drive measurable business outcomes. With this basic understanding in mind, let’s look closer at how AI and ML engineers differ in their daily responsibilities.
AI Engineer vs ML Engineer: What Each Role Does
AI engineers and machine learning engineers work side by side in many cases to develop intelligent, data-driven products. Think everything from personalized recommendations to autonomous systems.
Yes, these roles intersect in some areas. But each brings a unique skill set and focus, which cannot be disregarded.
Let’s take a closer look at each.
What Does an AI Engineer Do?
AI engineers design and integrate systems capable of simulating reasoning, perception, and decision-making using modern AI frameworks. Their daily work involves creating tools like voice assistants, computer vision platforms, and natural language processing (NLP) applications that operate seamlessly in real time. They ensure these systems can interpret complex data and deliver intelligent, context-aware responses.
What Does an ML Engineer Do?
ML engineers focus on the learning process itself. They design and train models that allow systems to adapt and improve with experience. This includes data preparation, feature engineering, model training, and building machine learning pipelines that empower continuous learning and optimization.
In essence, AI defines how a system behaves, while ML determines how it learns. Both roles rely on one another. AI engineers use ML models to drive intelligent behavior.
AI engineers depend on ML frameworks for intelligent behavior, while ML engineers rely on MLOps and deployment frameworks for model delivery and testing.
Real-World Applications of AI and Machine Learning Engineers
When AI and ML engineers combine intelligent design and data-driven development, they create technologies that solve complex problems across industries. The table below highlights how each role applies its expertise to deliver real-world impact.
| AI Engineer | ML Engineer |
|---|---|
| Designs diagnostic systems that combine image recognition with patient records | Builds churn prediction models using historical customer usage data |
| Develops AI tools to assess investment risks using market and client data | Creates anomaly detection systems for early equipment failure identification |
| Creates personalized shopping assistants that adapt to real-time user behavior | Optimizes demand forecasting through predictive analytics workflows |
AI and Machine Learning Engineers Shared Priorities:
Both roles prioritize ethical AI, transparency, and scalability. This ensures their systems are reliable, explainable, and responsibly deployed.
AI Engineer vs ML Engineer: Responsibilities and Work Scope
AI and ML engineers share the same goal: creating intelligent, data-driven technology. But their day-to-day responsibilities differ in terms of workflows and collaboration points. Understanding where one role ends and the other begins helps teams build the right mix of expertise and set realistic expectations.
Daily Responsibilities of AI and ML Engineers
AI engineers handle system-level intelligence. They design algorithms for reasoning, perception, and language understanding. Then, they integrate them into tools such as chatbots and automation platforms.
Machine learning engineers focus on data. They build, train, and validate models that recognize patterns and produce accurate predictions. Their work in model training ensures that systems continue to learn and adapt as new data becomes available.
In short: AI engineers deliver intelligent AI systems; ML engineers deliver the learning models that make those systems adaptive.
AI and ML Engineers: Typical Tasks and Workflows
AI engineers will be the ones to design scalable architectures, integrate external APIs, and ensure systems respond accurately in real time. They combine multiple components, including ML models, to create smooth and intelligent user experiences.
ML engineers focus on automation and performance optimization in their day-to-day tasks. As such, they manage MLOps processes, oversee CI/CD pipelines, monitor model accuracy, and retrain models when data changes.
AI and ML Engineers' Scope of Work and Collaboration
The scope of AI engineering spans system-level integration and user interaction. These engineers usually work with frontend, infrastructure, and security teams. The point is to ensure reliable performance in production environments.
Machine learning engineering focuses more on data integrity and model reliability. These professionals’ goals are that models remain consistent and outputs are reproducible.
Where are these roles tightly connected?
Data engineers prepare information, ML engineers develop models, and AI engineers bring everything together into fully deployed systems.
AI and ML Engineers Expected Results and Deliverables
An AI engineer succeeds when an intelligent system performs well in production. Think chatbots, predictive tools, or adaptive interfaces.
An ML engineer succeeds when a model is accurate, efficient, and deployment-ready. This serves as the engine behind the AI’s learning capability.
In some cases, the boundaries blur.
Projects involving generative AI or autonomous technologies may require hybrid professionals who can both build models and integrate them into complex architectures.
These are typically ML engineers with strong software-integration skills rather than a distinct new job type. These responsibilities are becoming more common as AI shifts toward greater autonomy and adaptability.
Pro tip: While the roles may blur, the best practices stay the same. Here are 7 timeless tactics to hire the right AI talent for your company.
AI Engineer vs Machine Learning Engineer: Skill Set Requirements
AI and machine learning engineers share many core technical skills, but each role focuses on different parts of the development process. Understanding the skills that define each discipline is key to building strong, capable teams.
AI and ML Engineers' Programming Proficiency
Both AI and ML engineers need strong programming foundations, though they apply them in different contexts.
Common languages include Python, R, Java, and C++. These are essential for developing scalable systems, building models, and managing model training workflows.
AI engineers should know how to write clean, modular, and performance-optimized code that supports complex, production-level environments. They should also be able to collaborate with multiple teams, making maintainability and reliability top priorities.
ML engineers, meanwhile, focus more on automation and reproducibility. They should know how to use tools such as Git, Docker, and CI/CD pipelines to manage version control and deploy models efficiently across various systems.
Familiarity with AI and ML Frameworks
AI engineers work with frameworks that facilitate reasoning and perception, including deep learning and natural language processing platforms. As we explained above, they should design intelligent applications such as computer vision systems, chatbots, or recommendation engines.
The frameworks they should be able to use include platforms like OpenAI Gym for reinforcement learning, OpenCV for computer vision, spaCy and Rasa for natural language processing, and Dialogflow for conversational AI. These tools help AI engineers integrate perception, reasoning, and interaction into complete intelligent systems.
ML engineers use frameworks focused on developing, training, and deploying models. Libraries such as TensorFlow, PyTorch, and MLflow are standard, while cloud-based tools like Amazon SageMaker streamline experimentation and scalability.
Some technologies, like Hugging Face Transformers, bridge both roles. They allow ML engineers to train advanced language models and AI engineers to integrate those models into broader intelligent systems.
AI and ML Engineers' Problem-Solving Abilities
Both AI and ML engineers rely on strong analytical and problem-solving skills. They regularly encounter challenges like unbalanced datasets, model drift, and algorithmic bias.
ML engineers typically focus on diagnosing and fine-tuning models to mitigate these issues, while AI engineers design systems with built-in safeguards to maintain reliability under uncertainty.
Soft skills such as collaboration, adaptability, and clear communication are equally important, especially for engineers preparing for AI or ML leadership roles. In fast-moving environments, you need solid alignment between technical and leadership teams. That way, your goals remain clear, workflows are efficient, and projects scale successfully.
To summarize, here’s how their skill sets compare at a glance:
| Category | AI Engineer | ML Engineer |
|---|---|---|
| Programming | Writes clean, modular, performance-optimized code for scalable systems. Focus on reliability and collaboration. | Automates workflows and focuses on reproducibility. Uses Git, Docker, and CI/CD for model deployment. |
| Frameworks | Works with AI frameworks for reasoning and perception (e.g., deep learning, NLP, computer vision). | Uses ML frameworks for model training and deployment (e.g., TensorFlow, PyTorch, MLflow, SageMaker). |
| Problem-Solving | Designs robust systems with safeguards to handle uncertainty and maintain reliability. | Diagnoses and fine-tunes models to reduce bias, handle drift, and improve performance. |
| Soft Skills | Strong collaboration across teams; ensures maintainability and scalability. | Focus on efficiency, adaptability, and clear communication for continuous optimization. |
AI Engineer vs Machine Learning Engineer – 2025 Salary Snapshot
If you’re deciding between a path in AI or machine learning, salary might be one of the biggest factors. Both roles pay extremely well, but the focus of the work shapes how compensation scales.
Here’s what the latest 2025 data says.
AI Engineer Salary
In the U.S., the average base salary for an AI Engineer is around US $175,262/year, with additional cash compensation (bonuses, etc.) averaging about US $35,333. This brings their total compensation to roughly US $210,595.
However, Built In reports their average salary to be US$179,365, with additional cash compensation being US$34,775. This leads to a total compensation for AI engineers of ~US $214,140.
Of course, location and seniority cause large variations. For example, an “AI Engineer V” in the U.S. has average compensation of ~US $176,264/year (25th–75th percentile range: ~US $161k to ~$186k) as of October 2025, according to Salary.com data.
Remember: In high-tier tech firms and staff/lead levels, total compensation can reach much higher figures (multiple hundreds of thousands, including equity).
Machine Learning Engineer Salary
According to Built In, the average salary for a Machine Learning Engineer in the U.S. is about US $158,147/year, with additional cash compensation of ~US $44,019. As such, the total compensation for ML engineers is around US $204,934.
You can expect some disparities between sources regarding the ML engineer salary, too.
On Glassdoor, for example, the base salary for US ML engineers is ~US $156,212/year. The range for experienced engineers goes up to ~US $243,742/year (without bonuses/stock) in some cases.
Other sources show a similar range. For example, the global salary index shows ~US $190,000 for ML Engineers (average across many profiles) in 2025.
Who Earns More, an AI or ML Engineer?
As you can see from the most recent data, both AI and ML engineers command very strong salaries. Their incomes can exceed US $150k base and US $200k+ total compensation in many U.S. markets.
However, AI Engineers typically have slightly higher average base salary numbers than Machine Learning Engineers in the datasets above.
The reason why AI engineers usually earn a bit more on average is that they handle broader system design and integration.
Even so, the gap isn’t huge and depends heavily on seniority (staff/lead vs individual contributor), company, equity, and region.
Bonuses, company size, and tech stack also impact the top end significantly.
How to Choose Between an AI Engineer and a Machine Learning Engineer for Your Project
Choosing between an AI engineer and an ML engineer depends on your project’s specific goals, what you want the system to achieve, and how it should learn or respond.
If your project involves designing intelligent systems that simulate reasoning or perception, like chatbots, natural language processing tools, or autonomous agents, an AI engineer is the right fit. They focus on integrating sensors, decision logic, and user interaction into a cohesive, intelligent framework.
If your main objective is prediction or classification, for instance, demand forecasting, fraud detection, or other predictive analytics tasks, an ML engineer is best suited to the job. They specialize in developing accurate, data-driven models that continuously improve with new information.
For complex or large-scale projects, like those involving generative AI or autonomous systems, combining both skill sets delivers the best results. The AI engineer manages architecture, interaction design, and integration, while the ML engineer ensures models remain reliable, efficient, and adaptable.
By balancing system design with model expertise, teams can build intelligent, flexible technologies that evolve with real-world use.
| Category | AI Engineer | ML Engineer |
|---|---|---|
| Project Focus | Systems that simulate cognition and interaction | Data-driven prediction and automation |
| Best For | Chatbots, decision agents, robotics, perception systems | Forecasting, fraud detection, and recommendation algorithms |
| Core Tools | Symbolic AI tools, deep learning libraries, integration APIs | TensorFlow, PyTorch, MLflow, feature engineering tools |
| Deliverables | Full intelligent systems ready for production deployment | Optimized models integrated into workflows |
| Team Collaboration | Leads system-level design and integration | Manages model training, tuning, and deployment |
Emerging Trends in AI and Machine Learning Careers
As AI engineering and ML engineering continue to evolve, so do the technologies, frameworks, and skills that define these fields. The boundaries between the two roles are gradually fading as tools become more integrated and intelligent systems more complex.
New Technologies and Frameworks Shaping AI and ML Engineering
Recent advancements such as AutoML, federated learning, and generative AI are transforming how engineers approach development. Job postings requiring generative AI expertise grew by over 1,800% in 2023, reflecting its rapid adoption across industries that still continues in 2025.
Multimodal systems that combine text, image, and audio inputs are also expanding what’s possible. These innovations are broadening the responsibilities of ML engineers and bringing their work closer to that of AI engineers. Similarly, federated learning empowers distributed model training across devices and teams. This leads to better collaboration while protecting data privacy.
Together, these technologies are narrowing the gap between AI and ML roles, as unified frameworks now support both intelligent system design and model optimization.
Future Skills for AI and ML Professionals
New skill sets are reshaping expectations in both disciplines. Prompt engineering, which is the ability to craft precise, effective instructions for large language models, has quickly become a core competency. With the rise of large language models (LLMs), creating these high-quality prompts is also becoming essential.
Equally important are emerging areas such as AI ethics, model interpretability, and MLOps. Engineers must design systems that are powerful, fair, explainable, and reliable in production environments.
Above all, continuous learning is key. With tools and frameworks changing rapidly, professionals who adopt experimentation and cross-disciplinary collaboration will be best positioned to lead the next wave of innovation in AI and ML.
Power Your Team with the right AI and ML Engineers
As AI and ML continue to move forward, the distinction between roles remains clear, though they are complementary.
What’s important is that both roles are essential together, turning intelligence into practical, scalable outcomes. As technologies advance and architectures grow more complex, collaboration between these disciplines will only become stronger.
The most successful teams are those that understand both perspectives. Building the future of AI systems requires more than technical ability. It demands an integrated mindset that connects data, design, and experimentation.
That’s where Alpha Apex Group comes in.
We believe that organizations that align leadership, culture, and technical capability see higher engagement, sustainable growth, and more scalable innovation. Together, these elements lay the foundation for a smarter, more connected future.
As AI and ML converge, the most impactful engineers will bridge data, intelligence, and creativity to build adaptive, forward-thinking systems that redefine what’s possible.
Ready to prepare your team for the next generation of intelligent systems? Let’s connect.
FAQs
1. How do I know if my company needs an AI or ML engineer first?
It depends on your goal. If you need predictions, like demand forecasts or fraud detection, start with an ML engineer. If you’re building intelligent products that interact, decide, or automate tasks, bring in an AI engineer first.
2. What’s the biggest hiring mistake companies make with AI/ML roles?
The biggest hiring mistake companies make with AI/ML roles is combining them into one vague position. This leads to confusion, burnout, and poor results. Define who owns model development and who owns system integration before you post the job.
3. How can I tell if a candidate truly understands production-level AI systems?
Ask for examples beyond prototypes. Strong engineers mention versioning, retraining, latency budgets, and post-deployment monitoring. Basically, they think of reliability, too, apart from research.
4. What signals show an AI or ML hire will align with business goals?
An AI or ML hire will align with your business goals if they can translate tech into outcomes. Instead of explaining “how a model works,” they explain how it saves cost, drives retention, or increases efficiency. In other words, strategic thinkers will focus on ROI.
5. How do I retain top AI and ML talent once hired?
To increase retention, keep your mission ambitious and the scope clear. Engineers stay when they see impact, autonomy, and technical growth. Stagnant projects and shifting priorities are what push them out.
