What is an AI developer?

For example, an AI developer working on a natural language processing (NLP) chatbot for a financial services company might start their day by reviewing logs that track how the chatbot has handled customer inquiries. They look for patterns in user behavior and adjust the chatbot’s logic or AI components to improve accuracy. Suppose the chatbot struggles with vague account-related queries. In that case, the AI developer might modify the model’s integration with a retrieval-augmented generation (RAG) system to pull more relevant information from the company’s database.

Throughout the day, the AI developer improves the chatbot’s responses by adjusting prompt logic, fine-tuning application programming interface (API) calls or integrating a sentiment analysis module to better gauge customer satisfaction. They might also optimize the chatbot’s performance by refining its interaction with cloud-based AI services or improving its ability to escalate unresolved issues to a human representative. Unlike a machine learning engineer, who would focus on retraining the model or modifying its neural network structure, the AI developer ensures that the model interacts smoothly with other components and aligns with business objectives.

AI developers frequently collaborate with software developers, product managers and data scientists to enhance AI-powered features. They conduct testing, monitor AI performance in production environments and refine models to improve accuracy and efficiency.

A successful AI developer needs a combination of technical expertise and soft skills. AI developers will also work in close collaboration with machine language and AI engineers and should be familiar with the fundamentals of those fields.

AI developers must be proficient in programming languages such as Python, Java and C++. Python is widely used due to its extensive machine learning and deep learning libraries such as TensorFlow, PyTorch and sci-kit-learn. Java is commonly used for big data processing and enterprise AI software, while C++ is preferred for high-performance computing tasks.

AI developers must be familiar with software architecture, version control tools—such as Git and GitHub—and best practices for project management in AI development. Experience with web development frameworks and API integration is also valuable, especially for deploying AI models in real-world applications.

AI developers must understand machine learning models and deep learning architectures, including neural networks, decision trees and support vector machines. A solid grasp of predictive modeling is necessary for developing AI systems that recognize patterns, classify data and generate intelligent responses. While such systems are traditionally under the purview of the machine learning engineer, an AI developer should understand the fundamentals. Developers should also explore generative AI, which powers ChatGPT and other text-based AI tools.

Developers must be skilled in data analytics, data cleaning and preprocessing techniques, including working with structured and unstructured datasets, performing data visualization and using SQL and NoSQL databases. While some of this work might be more suited to a data scientist, AI developers should understand the basics.

AI developers must have strong problem-solving skills to handle complex challenges in AI system design and optimization. They must be able to analyze AI-generated outputs, troubleshoot errors and refine machine learning models. The ability to think critically helps developers make informed decisions when selecting AI architectures and machine learning algorithms.

AI development is not just about writing code; it also requires creativity. AI developers must design AI-solutions that enhance automation, predictive analytics and decision-making in the healthcare, finance and robotics industries. Innovating new computer vision applications and optimizing AI software requires a combination of technical expertise and creative problem-solving.

AI is a rapidly evolving field, with new breakthroughs and technologies emerging constantly. Developers must stay up to date with advancements in machine learning, deep learning and generative AI to remain competitive. Continuous learning involves reading research papers, participating in AI communities, taking online courses and experimenting with AI software. Exploring platforms such as OpenAI, Hugging Face and Kaggle helps developers refine their technical skill set and stay at the cutting edge of AI innovation.

Becoming an AI developer requires a structured career path that combines formal education, hands-on experience and continuous skill development.

Education and academic background

A degree in computer science, artificial intelligence, data science, statistics or a related field provides the foundational knowledge needed for AI development. Many universities now offer specialized programs in machine learning, deep learning and NLP. Advanced degrees, such as a master’s in AI or data science, can further develop expertise in generative AI, big data and reinforcement learning. However, practical experience and a strong portfolio can be as valuable as formal education.

Develop programming and machine learning skills

AI developers must be proficient in programming languages to build and deploy machine learning models. Understanding machine learning and deep learning techniques is critical for AI development. Entry-level or beginner developers can start with supervised, unsupervised and reinforcement learning fundamentals before progressing to more advanced topics such as deep neural networks and transformer architectures.

Gain practical experience through projects

Hands-on experience is important for developing expertise in AI development. Working on real-world AI projects helps build problem-solving skills and allows developers to apply theoretical knowledge in meaningful ways. Examples of AI projects include:

• Developing a predictive model for stock price forecasting using historical market data
• Creating an AI-driven healthcare application for medical image classification
• Building an NLP chatbot using OpenAI’s GPT models or ChatGPT for customer service
• Designing a big data pipeline for fraud detection in financial transactions

Explore AI frameworks and development tools

Familiarity with AI development tools and frameworks is required. AI developers often work with:

• TensorFlow and PyTorch for building and training neural networks
• Hugging Face for working with NLP models and generative AI
• scikit-learn for implementing traditional machine learning algorithms
• Docker and Kubernetes for deploying AI applications in scalable environments
• GitHub for version control and collaboration in open source AI software projects

Build a portfolio and contribute to open source projects

A strong portfolio demonstrates expertise and practical experience. AI developers commonly use GitHub to showcase projects and AI deployment. Contributing to open source AI projects is also beneficial, providing opportunities to collaborate with industry experts while gaining exposure to best practices in project management and software engineering.

Earn AI certifications and professional development

Certifications validate knowledge and help AI developers stand out in the job market. Industry-recognized certifications cover essential AI and machine learning concepts, including generative AI, neural networks and AI applications in business settings. Examples include:

• Certified Artificial Intelligence Scientist (CAIS)
• Artificial Intelligence Engineer (AIE)
• TensorFlow Developer Certification
• Microsoft Azure AI Engineer Associate
• IBM AI Engineering Professional Certificate

Staying updated on AI research and industry trends

AI is a rapidly evolving field, with new technologies emerging continuously. Developers must keep up with advancements in machine learning, deep learning and generative AI. Reading AI research papers, attending industry conferences and exploring new developments in AI software help professionals stay competitive. Online platforms, such as Kaggle, Stack Overflow and LinkedIn, also provide networking opportunities and discussions on best practices in AI development.

Tools such as ChatGPT, GPT-4 and Stable Diffusion have expanded the capabilities of AI applications, automating content creation, accelerating software development and transforming how businesses interact with AI. For AI developers, generative models present new opportunities for fine-tuning, customization and integration into enterprise systems. There’s evidence that AI code assistants are also helping developers enjoy their work more.

At the same time, low-code and no-code AI development platforms are making AI more accessible to those without extensive programming expertise. Services such as Google AutoML, Microsoft Azure AI and IBM watsonx® help users build, train and deploy machine learning models using intuitive interfaces and prebuilt templates. These platforms reduce the complexity of model development, enabling faster prototyping and integration into existing workflows.

While low-code tools can streamline development, they lack the flexibility required to build complex AI applications requiring specialized architectures, high-performance tuning and domain-specific adaptations.

As more businesses implement machine learning models and use big data, the demand for AI developers will continue to rise. In healthcare, AI helps with medical diagnosis, drug discovery and patient monitoring. In retail, AI personalizes shopping experiences and optimizes supply chains. AI-driven predictive analytics is helping businesses anticipate market trends and make proactive decisions. Even project management tools are integrating AI to optimize workflows and improve productivity.

This widespread adoption means companies require skilled AI developers to build and maintain cutting-edge AI systems. Developers with expertise in machine learning algorithms, generative AI and big data are particularly in demand as businesses seek to use AI to gain a competitive advantage.

In 2024, the global AI market was valued at approximately USD 233.46 billion. Projections for 2025 and beyond estimate the market size to reach between USD 243.70 billion and USD 294.16 billion¹, while forecasts for 2030 anticipate the market expanding to between USD 826.70 billion and USD 1,811.75 billion, with some estimates suggesting it might approach USD 1 trillion by 2027².  

As a result, the demand for AI specialists is expected to grow significantly. Many businesses are struggling to find professionals with the necessary programming skills and project management experience to lead AI initiatives. Contrary to fears that AI will replace jobs, the World Economic Forum³ predicts that AI will create 97 million new jobs globally, and AI developers will play a key role in this shift.

AI developers work on implementing AI-driven features in applications, integrating machine learning models and writing the necessary code to deploy AI functionality in software. Their role often involves building AI-powered applications for specific business or consumer needs.

Conversely, AI engineers focus on the broader engineering and deployment aspects of AI systems. These include designing scalable architectures, managing cloud infrastructure, optimizing models for performance and ensuring the smooth integration of AI systems into enterprise environments. AI engineers often work on the Machine Learning Operations (MLOps) pipeline, deploying, monitoring and maintaining models effectively.

Key distinctions:

• AI developer: Primarily responsible for coding, testing and integrating AI models into applications. Requires proficiency in programming languages such as Python and Java, as well as knowledge of machine learning frameworks.
• AI engineer: Focuses on the engineering, optimization and deployment of AI systems. Requires expertise in cloud computing, infrastructure management and system scalability.

Software developers build general-purpose applications, working with programming languages and frameworks to develop software products that might or might not include AI functionality.

AI developers specialize in building and implementing AI-powered solutions. Their work involves integrating machine learning models, developing AI-driven features and fine-tuning AI algorithms for applications. While software developers might incorporate AI technologies into their projects, AI developers focus specifically on designing, optimizing and deploying AI models.

Key distinctions:

• Software developer: Works on a wide range of applications, including web development, mobile apps and enterprise software. Might use AI tools but does not specialize in AI model development.
• AI developer: Specializes in AI applications, focusing on integrating and implementing AI models and algorithms to enhance software functionality.