What AI Developer Skills Do You Need in 2024?
According to the latest Stack Overflow survey, AI developers are among the top earners in the software industry. Demand for them will continue to rise, as 76% of the respondents are using or planning to use AI tools in their development process.
The past two years have further expanded the possibilities of AI and how it can be used. AI has changed how businesses operate, approach marketing, sell their products, manage customer relationships and conduct research and development. The 2024 McKinsey State of AI Survey reveals that 72% of organizations have already adopted AI in one or more areas of their operations, which further stresses the importance of AI in modern software development.
Newer roles, such as AI engineers, machine learning (ML) engineers and MLOps, are becoming popular. They require traditional developers to expand their skill sets.
In this article, I’ll explore the essential skills you’ll need to become an AI developer. I’ll provide insights from Tim Faulkes, chief developer advocate of Aerospike. I’ll also explore the key trends shaping AI and how they affect your role as a developer, examine the intersection between AI and software development, and look into specialized skills for different developer roles.
Key Trends Shaping AI Development
Before exploring the skills you’ll need to become an AI developer, it’s important to consider some advancements in the AI ecosystem that are expanding the scope of these applications. The most significant trends (which I’ll break down below) include:
- Real-time data processing
- Vector databases
- Generative AI
- Convergence of AI with traditional development processes
Real-Time Data Processing
In industries such as healthcare, finance, autonomous vehicles and the Internet of Things (IoT) where decisions are made in microseconds, there has been a push to develop advanced AI systems. This need has led these industries to leverage technologies like Apache Kafka and Apache Flink to enable continuous streams of data, which feed pipelines of AI models, allowing instant predictions and updates.
Additionally, latency is a general problem in software systems, and AI systems are not immune to it. The need to bring computation and data storage closer to the data source also drives how AI systems are being developed.
As Aerospike’s Faulkes told SiliconANGLE, “The real-time data is here. Anyone who’s looked at any real-time decisioning, yes, you’re using AI to make the decisions typically. But the more data you can feed the pipelines, the better results you’re going to get.”
He’s seen this in his eight years working with developers at the multi-model database vendor. Real-time data has “come to the forefront recently because of recent innovations,” he said. “As people’s need for data has risen, the data scientists are saying, ‘If I can get 10 times as much data, I can give you a better fraud result,’ for example. So yes, the revolution is here.”
The takeaway: This means that as an AI developer, you must be skilled in technologies enabling real-time data processing, possess domain knowledge relevant to your projects and consider latency when building and deploying AI solutions.
Vector Databases
The way data is stored in a database significantly affects retrieval performance and efficiency. AI systems that deal with large volumes of data and integrate with machine learning models need vector databases, particularly for retrieval-augmented generation (RAG). These databases index and store data as high-dimensional vectors that AI systems can use to power search, recommendations and other retrieval use cases.
“Vectors are these mystical concepts, but they’re really easy,” Faulkes explained. “It’s just a set of numbers — let’s think of an image, I’ve got a picture, I run a lossy algorithm like JPEG on it. It loses information and turns it into a smaller representation of the same thing.
“That’s exactly what a vector does. It takes whatever input it has, be it an audio file or a question you give it, and it turns it into a series of numbers, and that’s all it is.”
But that series of numbers can be enormous. “We’ve got vectors that can be up to thousands of dimensions, and then you need a vector database. All they do is look at these hundreds or thousands of dimensions of vectors and say, ‘You’ve given me one. Find me the ones that are closest to it.’”
The takeaway: Vector databases are becoming more powerful and will continue to influence how AI applications are built. This means that as an AI developer, you must:
- Understand the purpose of vector databases.
- Choose the right one.
- Understand how vector databases accept and retrieve data.
- Know how to integrate vector databases with applications.
Generative AI
Generative AI (GenAI) systems can create content like texts, images, audio and videos. Over the past two years, GenAI has been widely adopted in product design, entertainment and synthetic data creation. While models such as GPT-4 and DALL·E 3 have met content creators’ needs to an extent, they will be pushed even further due to demand from creators. Their needs will keep changing and, in turn, the creators will improve existing models and create new and more advanced ones.
Under the GenAI category, you will also find prompt engineering and large language models (LLMs). LLMs are the underlying models that process and generate content based on their training data, and prompt engineering is the practice of crafting inputs to guide LLMs to produce desired outputs.
Faulkes finds it fascinating that you don’t program LLMs, at least in the traditional sense. Instead, you give them instructions in your native language about what you want them to do. Faulkes provided an example: “You say, ‘Hey, you’re an expert on Aerospike, and I want you to answer my questions in the best manner using this information.’ It’s not writing code, it’s writing English.”
There’s also a complete ecosystem around LLMs, he said. “You’ve got the LLMs, you’ve got the prompt engineering, you’ve got your vector databases so that you can get the right information out of all your inputs and give it the answer.”
The takeaway: This means that as an AI developer, you must:
- Understand generative AI capabilities.
- Know how to select appropriate tools and models.
- Follow all ethical and other responsibilities.
- Know how to integrate generative AI with applications.
AI’s Convergence With Traditional Development Processes
AI is changing the software development process, from AI-powered development tools like GitHub Copilot and Tabnine to MLOps, which integrates machine learning models into the software development life cycle (SDLC).
“Everyone’s used to the ChatGPT sort of things where they say, ‘Yes, I can give it some information and it’ll give me some answers, and it might be right or might be wrong. But how do I embed that into an application?’” Faulkes noted. “Plugging [AI] into your traditional applications and having the two liaised together in the right manner so you get the best application results … that’s really what I find exciting.”
The takeaway: This means that as an AI developer, you must learn the skills that will help you embed AI capabilities into conventional software and build applications that can learn from data, improve over time and provide smarter functionalities.
Skills Required To Become an AI Developer Today
AI developers are responsible for designing, developing and maintaining AI systems. These developers have become increasingly popular and are in wide demand because of their role in using AI to transform multiple industries. Below are the main skills you need to become an AI developer.
Programming Skills
Proficiency in programming languages like Python (most common for AI/ML), R (for statistics computing), and Java, C++ or Julia (for performance-critical applications) is a fundamental skill of an AI developer. You also need a good understanding of AI and machine learning libraries and frameworks like TensorFlow, PyTorch, scikit-learn and Pandas.
Mathematics and Statistics
As an AI engineer, you should be familiar with linear algebra, calculus, probability and statistics, and other computer science optimization theories. These are essential skills you need to design, build and maintain AI algorithms and techniques.
Data Handling and Analysis
Data is the bedrock of AI, and AI developers must understand how to properly collect, clean, normalize and transform data. Knowledge of SQL and NoSQL is also important since you’ll deal with structured and unstructured data.
Additionally, you should be familiar with big data tools and frameworks such as Apache Hadoop and Spark for storing, managing and processing big data.
Machine Learning and Deep Learning
As an AI developer, you must be skilled in training and evaluating model performance using metrics like accuracy, precision, F1 score and other machine learning techniques. Additionally, you should be familiar with ML algorithms like linear regression, logistics regressions and neural networks and deep learning (DL) algorithms like convolutional neural networks (CNNs) and generative adversarial networks (GANs).
Cloud Computing and Deployments
AI developers should be familiar with cloud-based ML and AI services offered by providers like Google Cloud Platform, AWS and Microsoft Azure. They provide prebuilt models, APIs, resources, vector databases and notebooks for prototyping and building AI applications.
Additionally, knowledge of tools like Docker and Kubernetes comes in handy when packaging and deploying models on these platforms.
Ethics and Bias in AI
AI developers need to understand the ethical implications of AI. You’ll be responsible for making AI systems fair, accountable and transparent in the data and models used for training the system.
Soft Skills and Continuous Learning
Beyond technical knowledge, you must be able to communicate effectively with nontechnical and technical stakeholders, collaborate with technology teams and keep up with the latest trends in AI and ML development.
While the skills above are tailored to AI specialists and AI-related companies, the shift in the AI ecosystem and its application across multiple industries has also affected how traditional software developers and companies build and leverage AI in their operations. Let’s look at that in detail.
How AI Is Shaping Software Development
For most developers, the first glimpse of the possibilities AI offers is through using automated code completion and generation tools like GitHub Copilot and Tabnine in their code editor or integrated development environments (IDEs). These AI tools are used to enforce standards, improve productivity and increase the pace of software development.
Beyond automating code generation, AI has also improved how developers:
- Use static analysis to fix security vulnerabilities, code quality issues and bugs. Examples include DeepCode and Snyk.
- Automate testing procedures and predict parts of the application that are most prone to malfunction.
- Tackle repetitive project management tasks like task assignments, progress tracking and reporting.
- Use automated code reviews to enforce coding standards and best practices.
The possibilities AI offers to developers are extensive, and most developers have used AI directly or indirectly in their operations.
Beyond developers leveraging AI tools to increase their productivity, companies are integrating AI into one or more areas of their businesses. This means developers can’t just be consumers of AI technology; they must also know how to integrate AI into business requirements.
How Accessible Is AI to Software Developers?
When companies want to build AI products or extend some areas of their business with it, their go-to people include AI experts, including AI engineers, ML engineers, data scientists, research scientists and deep learning engineers. This approach is usually expensive and difficult to scale.
However, AI has become more accessible to software developers, with companies such as OpenAI, Cohere and AWS offering:
- Pretrained models and APIs
- Transferred learning
- Low-code/no-code AI platforms
- Developer tools
- Improved documentation and learning resources
Despite the accessibility these companies offer, both beginner and experienced developers need to understand how AI works and how it’s being used to power these tools.
“There are so many moving pieces, and they’re so novel,” Faulkes said. “How do they hang together? It’s almost frustrating for experienced developers. You hear all these terms, [but] how do you put them together? How do you get a generative AI application that’s going to work at scale when you don’t understand what large language models and vectors and things like that are? We’re not used to doing fuzzy things; we’re used to getting an answer out of a computer.”
The possibilities offered by AI companies will continue to shape the roles and responsibilities of developers who want to build the next generation of modern software.
How AI Is Transforming Software Developers’ Roles and Responsibilities
Pretrained models and APIs, transferred learning, low-code/no-code AI platforms and other AI-enabling solutions are blurring the lines between the traditional roles of the developer and the AI specialist. Developers across the board must have at least a basic understanding of AI concepts and how to leverage them in their domain.
Let’s look at how AI is shaping the roles and responsibilities of various developers.
Frontend Developers
As a frontend developer, you can leverage AI when building features such as chatbots, personalized recommendations, voice commands and conversational interfaces. These AI-powered elements enhance interactivity and offer a more personalized user experience.
Additionally, you are expected to be familiar with AI-driven tools that can generate layouts, optimize user interfaces and ensure web accessibility standards are met automatically.
AI skills required by frontend developers include:
- Proficiency in frameworks and libraries like TensorFlow.js and Brain.js for embedding AI features on the frontend.
- Familiarity with AI-based APIs and how to integrate them.
- Knowledge of AI-powered layout generation and prototyping tools.
Backend Developers
As a backend developer, you are expected to design, develop and maintain infrastructure that reduces the latency of AI systems that require real-time processing and analytics. You’ll also ensure that the APIs that the system is consuming and exposing are secure and free of malicious parties.
Additionally, you are expected to be familiar with AI-powered tools to predict and prevent potential system failures.
AI skills required by backend developers include:
- Familiarity with MLOps tools and best practices for managing AI models.
- Knowledge of ML frameworks and experience integrating ML models into backend systems.
- Expertise in big data and real-time data processing technologies like Apache Kafka and Apache Flink.
- Knowledge of extract, transform and load (ETL) processes for integrating data from multiple sources.
Full Stack Developers
As a full stack developer, you are expected to build applications that leverage AI capabilities both on the client side and server side and ensure seamless integration between the components. You’ll use AI-powered tools to test and debug across the full stack.
AI skills required by full stack developers:
- Proficiency in integrating AI-powered APIs and services across full stack development.
- Knowledge of AI frameworks across both the client and server side of the application.
- Familiarity with DevOps and MLOps for managing and deploying AI applications.
- Knowledge of data handling and ETL processes.
Next Steps
Advancements in AI engineering and the opportunities it brings will continue to grow. It will shape both the roles and responsibilities of AI specialists such as AI developers, and also cut across traditional developer roles. Industries such as healthcare, finance and autonomous vehicles leveraging AI solutions will also influence the skills required of developers.
To learn more about the important role databases play in the development and deployment of AI applications, check out “An insider’s guide to AI databases.”