AI Engineer Vs. ML Engineer: Differentiating Between Roles

Have you ever wondered how often people mention artificial intelligence and machine learning engineering interchangeably?

It might look reasonable because both are based on data science and significantly contribute to highly intelligent systems, overlapping with each other at some points.

The thing is that this resemblance complicates understanding the difference between AI and machine learning concepts, which hinders spotting the right talent for the particular needs of companies.

AI is a broad umbrella covering multiple segments, and Machine Learning is a subset of AI that allows machines to learn from data. 

Fundamentally speaking, when you hire an AI engineer, they’ll be responsible for implementing AI into solutions on a broader scope. Meanwhile, machine learning engineers typically develop and improve learning models.

So today, we’ll decompose each of them and discuss how beneficial they can be for businesses.

Comparing the roles of AI and ML developers

The sections below break down and analyze these positions’ responsibilities, skills, education, and job opportunities.

Understanding the role of an AI engineer

It takes a lot to become a well-grounded expert in the artificial intelligence area.

Scope of work and responsibilities

What are the commitments here?

1. Requirement analysis and project assessment; 
2. Data collection and preprocessing for accuracy and consistency;
3. Model selection and development for performance customization;
4. Model evaluation and validation to confirm trustworthiness;
5. AI model deployment and integration to secure reliability and scalability;
6. Continuous monitoring and maintenance for appropriate accuracy;
7. Collaboration and communication in dedicated development teams to stay on the same page;

It’s only a slight dive-in into artificial intelligence developers’ duties. A more detailed description is covered in our article on AI engineers’ roles and responsibilities.

Key skills for AI engineers

The following is a teeny-tiny list of skills crucial for AI engineers.

• Model development and optimization to create and fine-tune models for better accuracy, speed, and efficiency;
• Programming proficiency in languages like Python, R, and Java.
• ML algorithms for predictions and data-based decisions;
• Deep Learning expertise to analyze unstructured data, such as images, audio, and text;
• Mathematics and statistics. design accurate models with real-world pattern reflection;
• Neural networks to identify patterns and relationships in data for complex tasks like facial recognition, fraud detection, or product recommendations;
• Data handling and feature engineering to design advanced data inputs for higher model performance;
• Algorithmic scalability and optimization to keep up with speed and accuracy throughout expanding data volumes;
• Cloud computing and AI deployment for remote model management to provide deploy accessibility and scalability;
• AI tools, frameworks, and libraries (TensorFlow, PyTorch, Scikit-learn, and other AI development tools) to streamline complex AI tasks);
• MLOps to oversee the lifecycle of AI models in production for constant accuracy and relevance;
• Computer Vision and NLP to interpret visual data and assist with human language comprehension and generation.

No need to mention that the range of skills is usually reflected in an AI engineer’s salary.

Education and certifications for AI engineers

Higher education base. AI engineers need a strong academic foundation to deeply comprehend the main technology principles and their applications.

1. Computer Science/Software Engineering (Bachelor’s) are good starters for an AI engineer, giving them core skills for creating highly intelligent solutions including programming, algorithms, data structures, databases, system design, operating systems, and software development.
2. Mathematics/Applied Mathematics (Bachelor’s) gives a solid understanding of the mathematical principles, which is the basis for AI and machine learning, covering areas of algorithms and data analysis like linear algebra, calculus, probability, and statistics.
3. Data Science (Bachelor’s) amplifies a fundamental AI aspect – management, analysis, and interpretation of large data sets, giving strong knowledge of machine learning, data visualization, big data processing, and statistics for designing AI models and deriving insights from data.
   
4. Artificial Intelligence/Machine Learning (Master’s) gives programmers a deeper dive into this field, suggesting stronger knowledge of comprehensive AI algorithms like DL, reinforcement learning, and natural language processing (NLP).
   
5. Computer Science with an AI Specialization (Master’s) focuses on broader computer science topics and strong knowledge of AI practices, covering both theoretical and practical aspects of AI, such as ML, computer vision, and robotics.
   
6. Artificial Intelligence, Machine Learning, or Robotics (PhD) is mostly a first choice for programmers deeply involved in academic research or high-level AI development, including original research, where they elaborate on new AI algorithms or solve complex AI-related problems.

Certifications. Specialized certifications for AI engineers help them get a deeper background in focused areas and validate their expertise.

Google Professional Machine Learning Engineer implies developers’ knowledge of design, building, and deployment of ML models using Google Cloud tools. It includes subjects like data engineering, model optimization, and deployment in real-world conditions. Developers working in environments that apply Google Cloud for their intelligent solutions would benefit the most from it. 

IBM AI Engineering Professional Certificate by Coursera allows programmers to create smart systems with Python and open-source tools. It includes a practical background in DL, ML, and NLP areas and emphasizes building smart systems for real-world use cases.  

Microsoft Certified: Azure AI Engineer Associate is a perfect fit for engineers who want to focus on AI development with Microsoft’s Azure platform. It includes matters like the usage of Azure’s AI services for building, training, and deploying AI models, enhancing automation, and machine learning tasks.

AWS Certified Machine Learning – Specialty implies engineers’ expertise in designing and deploying ML solutions with AWS instruments. Being a good choice for developers who use the AWS toolset in AI development, it involves data engineering, exploratory data analysis, and deploying models with AWS services like SageMaker.

Stanford University’s AI Graduate Certificate (online) gives developers a strong background in AI and ML, covering subjects like neural networks, DL, and reinforcement learning. It’s perfect for engineers seeking to expand their expertise with advanced, research-driven AI concepts.

And of course, we shouldn’t underestimate the significance of practical experience that developers get throughout their internships, pet projects, and learning camp initiatives – and constant learning of new technologies combined with the willingness for personal development and self-education.

For instance, engineers on our clients’ projects regularly gain new certificates, learn new models and updates on them announced by their vendors such as OpenAI’s ChatGPT, Microsoft Copilot, etc, and even lecture these disciplines as professors in higher educational institutions.

Jobs and prospects

The role of AI engineers comprehends a ton of different responsibilities, skills, and backgrounds, which enables developers to boost or alter their career in AI.

Data scientist. These experts work more closely with data and analyze it to capture insights and trends with ML technology and statistical techniques. They design predictive models and adjust processes, which assist businesses with data-driven decisions and enhance products and services.

Computer Vision engineer. These developers build AI-driven systems that interpret visual data like images or videos. Their work empowers applications like facial recognition and autonomous vehicles, enhancing security, customer experience, and automation in different industries.

NLP engineer. These people create systems that comprehend and process human language, such as chatbots and language translation tools. They assist businesses with customer service automation and assess text data for better understanding, enhancing relations and decision-making.

AI research scientist. This field of AI activity reflects new AI approaches and algorithms, extending the capabilities of DL, reinforcement learning, and other related technologies. They are AI’s driving force that shows innovations later becoming selling-point technologies.

Robotics engineer. These developers create AI-powered robots mostly for task automation in manufacturing, healthcare, and logistics. Their elaborations boost performance by designing robots for independent task conducting, lowering costs, and improving efficiency.

AI product manager. This role is responsible for AI product development supervision, providing product/market fit and business goals alignment. They connect technical teams with stakeholders, leading projects to successful delivery.

AI consultant. These are more into advising companies on how to apply AI for challenge solving. They assist businesses with AI adoption for efficiency improvement, cost reduction, and growth.

AI solutions architect. They assemble the technical infrastructure for AI system deployment, securing easy integration with already operating elements and gears. Architects build scalable smart systems for different business requirements.

Important to note that all the above concerning AI developers is only average and typical of how things are, but not limited to it.

Exploring the role of a Machine Learning engineer

Now, let’s dig deeper into everything essential for ML engineers to comprehend throughout their careers.

Scope of work and responsibilities

When businesses hire ML engineers, they expect these developers to be responsible for quite a large amount of data and model-related activities.

• Data preprocessing and cleaning to secure the data fit for training;
  
• Building and training  ML models to ensure the model’s precision and suitability;
  
• Model evaluation and optimization to provide high performance in real-world scenarios
  
• Experiments and research to enhance the model’s performance and maintain the model updated with AI progress;
  
• ML model deployment meanwhile securing a smooth model integration to already running systems;
  
• Scaling ML solutions and adjusting infrastructure to support efficient routines with the system’s expansion;
  
• Model maintaining, fine-tuning, and updating to enhance accuracy and guarantee its ongoing alignment with business needs;
  
• Across-the-team collaboration to confirm the ML solution’s alignment with the company goals and its value delivery.
  
• Ensuring model interpretability and transparency for stakeholders to see its trustworthiness in decision-making approach;
  
• Managing cloud and on-premises environments to ensure their smooth and secure operation and sustain the model’s deployment.

Key skills for ML engineers

Core ML engineering skills usually reflect the distinctive workflow of the role.

• Programming mastery (Python, R, Java, and C++ along with TensorFlow, Keras, and Scikit-learn) to build and implement machine learning algorithms;
  
• Mathematics and statistics to create and optimize algorithms and comprehend ML model operations from technical PoV;
  
• Data preprocessing and cleaning to provide the accuracy and usability of the input for training models.
  
• Model deployment and monitoring to secure a smooth model operation and keep it relevant to the latest updates for better effectiveness;
  
• ML algorithm proficiency (including supervised, unsupervised, and reinforcement learning approaches) to create effective models for various business demands;
  
• DL expertise (neural networks and convolutional neural networks) for jobs like image and speech recognition to design complex models;
  
• Model evaluation and validation (accuracy, precision, recall, and F1 score metrics, cross-validation, and overfitting) to level up model generalization to new data;
  
• Data visualization tools (Matplotlib, Seaborn, and Tableau) to understand trends, recognize issues, or convey model training conclusions and deliver these findings to stakeholders;
  
• Cloud computing and infrastructure to deploy scalable and efficient ML solutions to production environments.
  
• Big Data instruments (Hadoop, Spark, or Kafka) for scalable data processing and management;
  
• Software engineering skills (version control, testing, and code writing) for model deployment and scaling into a production system;
  
• Problem-solving and critical thinking to convert business problems into technical solutions, fix issues, and constantly boost model performance.
  
• Bias mitigation to provide fair and transparent models for trustworthy decision-making, especially in data-sensitive environments.
  
• Continuous learning and research to implement advanced approaches and consistently boost models’ performance.
  

Education and certifications for ML engineers

Higher education base. As supposed, ML engineers need a deep educational background.

• Computer Science (Bachelor’s) includes fundamental matters like programming, algorithms, data structures, and software engineering, equipping developers with core skills in coding, problem-solving, and computational systems, with relevant industry-focused courses.
  
• Statistics (Bachelor’s) emphasizes data analysis, probability, regression, and hypothesis testing, suggesting a strong basis for statistical approaches required for data analysis and ML model evaluation.
  
• Electrical Engineering (Bachelor’s) offers a reliable background in signal processing, control systems, and systems design, focusing on robotics, computer vision, and autonomous systems, requiring hardware integration and algorithm optimization.
  
• Machine Learning (Master’s) provides particular training in machine learning algorithms, DL, reinforcement learning, and NLP, giving both theoretical facts and practical basis to create and employ progressive ML models.
  
• Data Science (Master’s) amplifies data analysis and machine learning disciplines combined with data wrangling, big data technologies, and model building, providing students with the instruments for ML applications in real-world data-driven scenarios.
  
• Computer Science/AI (PhD) is research-focused and aims at original contributions to the domain, being a perfect match for experts striving for advanced research or highly specific positions in areas like deep learning or AI innovation.

Certifications. Specific certifications deepen ML engineers’ knowledge and demonstrate their expertise in particular subjects.

Google Professional Machine Learning Engineer is a comprehensive exam that evaluates an ML engineer’s proficiency in creating ML models, training them with Google Cloud tools, and deploying them effectively. It’s valuable for engineers looking to confirm their knowledge of ML concept applications with cloud platforms.

AWS Certified Machine Learning – Specialty is created for experts who build, employ, and sustain ML solutions with AWS. It includes subjects like data engineering, machine learning modeling, and deployment.

IBM Data Science Professional Certificate by Coursera equips developers with a powerful basis in related technologies, also covering programming instruments like Python and libraries such as Pandas, NumPy, and Matplotlib. It’s the first choice for programmers who need extensive, beginner-to-intermediate knowledge of ML approaches and instruments.

Career growth and specialization opportunities for ML experts

Due to the combination of quite broad yet specific skills and the scope of work of ML engineers, they overlap with quite some data-related roles.

Data engineer. These experts design and sustain the systems that enable efficient collection, storage, and processing of large amounts of data. They are responsible for preparing the data infrastructure for ML developers to work with, providing the data clarity and readiness for analysis.

Data scientist. They analyze and decompose intricate data to empower businesses for better decisions. These engineers usually design and apply ML models to spot patterns, make predictions, and derive insights, functioning across such data-sensitive fields as finance, marketing, and healthcare.

Robotics engineer. This type of engineer creates and elaborates on robots and automated solutions. Throughout their work with ML technology, they aim at robots’ capability to self-educate and adapt through AI, including object recognition or data inputs-based decisions.

Business Intelligence developer. These engineers are crucial for building systems and reports that empower businesses for data-driven decisions. Using data analytics and visualization instruments to assist companies with trends and pattern identification, these engineers also integrate ML models for prediction improvement.

Computer vision engineer. These developers elaborate on technology that enables computers to comprehend and interpret visual information, such as images and videos. Here, former ML engineers might train models to empower applications like object detection, facial recognition, and medical image analysis, using ML processes.

Natural Language Processing engineer. This kind of expert focuses on enabling computers for human language comprehension and processing, such as text or speech. They employ models to enhance applications like voice assistants, chatbots, or language translation services.

Common ground and key differences between AI and ML engineering

So, what’s more there about these roles – similarities or distinctions? Let’s break this down.

Similarities and overlapping skills

Let’s take a look at how broad the common ground between AI engineers and machine learning engineers is.

Programming proficiency. Both of these roles demand strong programming skills, particularly in Python, Java, C++, and R, to design, test, and deploy their models.

ML algorithms and models. Both artificial intelligence and machine learning experts preserve a deep knowledge of algorithms like linear regression, decision trees, random forests, SVMs, and k-means clustering. While AI engineers apply these as elements of broader AI systems, ML developers emphasize them for particular tasks.

Mathematics and statistics. A strong background in linear algebra, calculus, probability, and statistics is a must for these roles. Both AI and ML professionals require this expertise to develop, optimize, and comprehend algorithms applied to these technologies.

Deep Learning expertise. Both roles include DL frameworks like TensorFlow, Keras, and PyTorch in their work to create and gauge neural networks. While AI engineers mostly use this mastership for broader applications, ML experts amplify model optimization for particular tasks.

Model development and optimization. Both kinds of engineers are into model development and optimization. These activities involve hyperparameter tuning, cross-validation, and securing the model’s accuracy and efficiency. While AI programmers incorporate models into larger smart solutions, ML developers aim at model optimization for particular outputs.

Data preprocessing and feature engineering. Both development roles require a deep understanding of data preparation and management. To improve model performance, programmers need to have strong expertise in data cleaning and feature extraction regardless of whether the application is for a machine learning model or a broader AI system.

Cloud computing and model deployment. Both engineers are supposed to be familiar with model deployment on such cloud platforms as AWS, Azure, and Google Cloud. containerization tools like Docker and Kubernetes are also applicable for scalable deployment and efficient model execution.

Big Data technologies. Large dataset management is an integral aspect for both roles, implying mastery of big data technologies like Hadoop and Apache Spark. Both roles require processing and analysis of data bulks.

AI/ML frameworks and libraries. Both types of engineers use libraries like Scikit-learn, XGBoost, and LightGBM. While AI developers apply them as part of more extensive solutions, ML programmers take these instruments, particularly for designing and enhancing ML models.

MLOps and model monitoring. Both roles include MLOps elements – the practice of deploying, tracking, and handling ML models in production environments. Both of them are supposed to secure constant model performance and retraining as required.

Core differences between AI engineers and ML engineers

Although the worlds of AI and ML intersect at some points, each of them is genuinely distinctive.

Scope of work and project focus

AI engineers have a broader focus area and aim at full-cycle AI project management. Their duties include integrating AI components into current systems, ensuring ethical AI standards, and fulfilling business needs.

ML engineers focus on creating, training, validating, and deploying machine learning models. Data-driven work like prediction, classification, and recommendation is their main concern.

Skill set

AI engineers need expertise in multiple AI technologies, including but not limited to machine learning, deep learning, neural networks, robotics, and cognitive computing. They also need an understanding of how to incorporate these solutions into applications resembling human intelligence.

ML engineers must concentrate specifically on ML concepts, covering supervised learning, unsupervised learning, reinforcement learning, and neural networks. They also have a strong understanding of algorithms, statistical models, and data processing.

Tools and technologies

AI engineers can use and commonly operate specialized hardware like GPUs and TPUs, to speed up AI model training and implementation. They also apply tools for model management, deployment, and monitoring.

ML engineers often trust big data agents like Hadoop and Spark to process and examine vast datasets. Machine learning frameworks tailored for machine learning model development and training like TensorFlow, PyTorch, and Scikit-learn are also their tools of choice.

Approach to problem-solving

AI engineers build intelligent systems capable of autonomous decision-making and adaptation to new scenarios. They also elaborate on comprehensive systems, including autonomous vehicles, chatbots, or smart assistants, which demand a blend of AI techniques.

ML engineers work with data-driven challenges to design solutions that consistently enhance. They aim to create models that can be extracted from training data for predictions or new data classification.

Application areas

AI engineers typically participate in projects to integrate AI models into autonomous systems like self-driven cars or robots.

ML developers extract data and make prediction, classification, or recommendation-related tasks, such as fraud detection, customer segmentation, or product recommendations.

Depth of data science involvement

Artificial intelligence engineers generally obtain quite a deep understanding of data science principles. Yet, their expertise in data processing and analysis may be lower than that of ML engineers.

ML engineers have a strong background in these concepts and high skills in data preprocessing, feature engineering, and model evaluation.

End goals

Artificial intelligence experts use AI to build systems that mimic human intelligence or decision-making, elaborating on comprehensive, multi-faceted projects that cover multiple AI subfields – for instance, robotics + computer vision + ML.

ML programmers design robust models that self-educate from data and gradually enhance performance. They mostly focus on data quality, algorithm optimization, and model evaluation.

Interdisciplinary collaboration

AI engineers work in interdisciplinary teams involving data scientists, software engineers, and domain experts, building AI applications across such data-driven industries as healthcare, robotics, finance, etc.

ML engineers work side-by-side with data scientists, software developers, and business analysts, yet with a stronger emphasis on model deployment, scaling, and optimization in production environments.

To sum up the differences:

AI engineers might work on a wider variety of AI technologies and systems built to mimic human intelligence.

ML programmers focus on ML algorithms, educating machines to learn from data to create and adjust predictive models.

While both roles involve data and algorithms, AI developers usually handle more comprehensive, interdisciplinary problems, as ML experts concentrate particularly on gauging and fine-tuning ML models.

Final words on what role to hire

With the evolving advancement of AI and ML technologies, new roles with broader opportunities will appear. As companies continuously adopt data-driven solutions, the demand for AI and ML engineers will continue to grow. All combined makes finding the necessary talent and bridging the gap a critical point for the business.

So let’s sum up key takes on how to hire for a necessary role:

1. Evaluate your particular needs, regarding the essence of the project and the shared skills of your current team.
   
2. Scan for industry trends and estimate talent availability in the region for informed decision-making.
   
3. Consider the advantages of each role within project-specific needs, ROI aspects,  and long-term strategic partnerships.
   

In case you’re on short time and human resources span, you can always count on Mobilunity recruiting services.

Written by:

Kateryna Hritsai

Market Researcher