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June 12, 2024

What is an AI Engineer: A brief overview

John Kim
Co-founder @ Paraform

Artificial intelligence (AI) is everywhere these days, from the recommendation systems on your favorite shopping site to the virtual assistants answering your questions. But how do these intelligent systems come to life?

Enter the AI engineer. Let’s briefly go over what they are and what they do.

Who is an AI Engineer?

An AI engineer is a specialized type of software engineer who focuses on developing and implementing artificial intelligence systems.

These systems are designed to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding.

AI engineers leverage machine learning algorithms, neural networks, and other AI techniques to create intelligent applications that can make decisions, recognize patterns, and even predict future outcomes.

Core Responsibilities of an AI Engineer

The role of an AI engineer is multifaceted and involves a variety of tasks and responsibilities.

Here are some of the core duties:

1. Developing AI Models and Algorithms

One of the primary responsibilities of an AI engineer is to design and implement AI models and algorithms. This involves selecting appropriate machine learning techniques, such as supervised, unsupervised, or reinforcement learning, and developing models that can process data and generate predictions or classifications.

2. Data Preparation and Processing

AI systems rely heavily on data, and preparing this data is a critical task. AI engineers are responsible for collecting, cleaning, and organizing large datasets.

They ensure that the data is of high quality and suitable for training AI models. This process often involves dealing with missing values, normalizing data, and splitting datasets into training and testing sets.

3. Training and Tuning AI Models

Once an AI model is developed, it needs to be trained using the prepared data. AI engineers train models by feeding them data and adjusting parameters to optimize performance. This process, known as hyperparameter tuning, is crucial for improving the accuracy and efficiency of AI models.

4. Deploying AI Systems

After developing and training AI models, the next step is to deploy them into production environments. AI engineers ensure that the models can operate efficiently in real-world scenarios. This involves integrating the models into existing systems, monitoring their performance, and making necessary adjustments.

5. Maintaining and Updating AI Systems

AI systems require ongoing maintenance to ensure they remain effective and up-to-date. AI engineers regularly monitor system performance, address any issues that arise, and update models as new data becomes available.

This continuous improvement process is essential for maintaining the reliability and accuracy of AI applications.

Key Skills for AI Engineers

To succeed as an AI engineer, one must possess a diverse set of technical and non-technical skills. Here are some of the key competencies required:

1. Programming Skills

Proficiency in programming languages such as Python, R, and Java is essential for AI engineers. These languages are commonly used for developing AI models and algorithms. Python, in particular, is favored for its extensive libraries and frameworks, such as TensorFlow, PyTorch, and sci-kit-learn.

2. Mathematics and Statistics

A strong foundation in mathematics and statistics is crucial for understanding and applying machine learning algorithms. AI engineers have to be well-versed in concepts such as linear algebra, calculus, probability, and statistical analysis.

3. Machine Learning and Deep Learning

Knowledge of machine learning and deep learning techniques is fundamental to the role of an AI engineer. This includes understanding various algorithms, such as decision trees, support vector machines, neural networks, and reinforcement learning. Familiarity with deep learning frameworks like TensorFlow and PyTorch is also important.

4. Data Engineering

AI engineers must be skilled in data engineering, which involves managing and processing large datasets. This includes expertise in SQL, NoSQL databases, data warehousing, and ETL (Extract, Transform, Load) processes.

5. Software Development

Strong software development skills are necessary for designing, building, and maintaining AI systems. This includes knowledge of software engineering principles, version control systems (e.g., Git), and agile development methodologies.

6. Critical Thinking and Problem-Solving

AI engineers must be adept at critical thinking and problem-solving. They need to analyze complex problems, devise innovative solutions, and troubleshoot issues that arise during the development and deployment of AI systems.

7. Communication and Collaboration

Effective communication and collaboration skills are essential for AI engineers, who often work in multidisciplinary teams. They need to explain technical concepts to non-technical stakeholders, collaborate with data scientists and software developers, and contribute to project planning and management.

Job Titles

AI engineers can hold various job titles, such as:

  • Machine Learning Engineer: Focuses on designing and implementing machine learning models.
  • Data Scientist: Specializes in extracting insights from data using statistical and machine learning techniques.
  • AI Research Scientist: Conducts research to advance AI technologies and develop new algorithms.
  • AI Consultant: Provides expertise to organizations on how to leverage AI to solve business problems.
  • AI Product Manager: Oversees the development and deployment of AI products and solutions.


AI engineering is one of the most lucrative fields in technology. According to various industry reports, the average salary for AI engineers in the United States ranges from $100,000 to $150,000 per year, with experienced professionals earning significantly more. The job outlook for AI engineers is highly positive, with a projected growth rate much faster than the average for all occupations.

Tips for recruiting AI Engineers

Know What You Need:

  • Define the role: Clearly outline the responsibilities, required skills (hard and soft), and experience level for the AI engineer position.
  • Tailor your search: Are you looking for a seasoned expert or someone with strong fundamentals? Understanding your needs helps target the right talent pool.

Assess Skills Effectively:

  • Technical assessments: Utilize online coding challenges or take-home projects to evaluate an AI engineer's practical skills.
  • Focus on problem-solving: Design interview questions that assess a candidate's ability to approach and solve complex AI problems.
  • Portfolio power: Encourage candidates to showcase their past projects and contributions to get a well-rounded understanding of their experience.

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