Introduction

Artificial Intelligence, or AI, is the capability of a computer system to perform tasks that are typically thought to require human intelligence. It can be subdivided into categories like machine learning, deep learning, natural language processing (NLP), computer vision, robotics, and expert systems.

AI has rapidly become one of the most sought-after tech stacks, but despite its popularity, challenges remain when hiring AI developers. Supply and demand poses the biggest challenges, with the demand for qualified, experienced AI engineers outweighing the domestic supply within the US. Businesses often amplify this challenge by restricting their search to local tech talent; however, in recent years, the rise of remote, global work has begun to solve this issue.

What is AI?

AI can be broadly defined as when a computer is able to perform a task that is thought to require human-level intelligence. Most frequently, the AI that is currently being implemented is Generative AI, or genAI, which creates original content like text, images and code by identifying patterns in large datasets and applying these patterns to prompts.

Other examples of popular applications of artificial intelligence include virtual assistants like Siri; search engines that have begun to use AI to rank results and provide relevant information; recommendation systems used by services like Netflix and Amazon; and self-driving cars, also known as autonomous vehicles.

Why use AI?

Artificial intelligence has myriad applications across every industry. Overall, it contributes to productivity by speeding up tasks that typically require human involvement, from controlling autonomous vehicles to automating administrative tasks.

Some applications of AI include:

• Agriculture: Companies like Blue River Technology and Harvest CROO Robotics are applying artificial intelligence to precision agriculture, for object detection, disease and pest detection, and the development of ‘smart’ devices like sprayers and harvesters. (Source: https://edis.ifas.ufl.edu/publication/AE529)
• Autonomous Vehicles: Tesla’s Autopilot uses AI to enable semi-autonomous driving on current vehicles, with fully autonomous vehicles being developed by Alphabet’s Waymo, General Motors and Cruise.
• Recommendation Systems: Platforms like Netflix and Spotify use AI to generate personalized recommendations for users based on their data.
• Fraud Detection: AI systems can sift through huge datasets to identify irregular patterns and anomalies that can indicate and resolve fraudulent activity. (Source: https://www.digitalocean.com/resources/articles/ai-fraud-detection)
• Robotics: AI-powered robots are currently deployed across the manufacturing industry to improve performance, and AI is also being applied to humanoid robotics by companies like Boston Dynamics and Figure AI.
• Chatbots: E-commerce platforms and other sites are increasingly using AI-powered chatbots to improve customer service experiences and resolve common issues more efficiently.
• Edtech: Platforms like Simplilearn use AI to offer course recommendations and provide personalized feedback to students.
• IoT: AI powers features in many IoT devices, such as Nest thermostats, which use artificial intelligence to learn users’ temperature preferences and schedule patterns.
• NLP: Natural Language Processing is used to power functions like translation and sentiment analysis for companies like Grammarly.
• Facial Recognition: Face recognition technology like Apple’s Face ID use artificial intelligence to identify and verify individuals for security purposes.
• Medtech: AI is being applied to medical research for various purposes, such as the novel protein sequencing performed at Generate Biomedicines for immunology research.
• Gaming: AI is frequently used to power NPC behavior in games such as The Last of Us Part II.
• Social Media: Platforms like Instagram and Tiktok use AI in their content algorithms, recommendation engines and search functions. 

Benefits of Artificial Intelligence

AI is increasingly used across industries and applications. Some of the major benefits include:

• Increased Efficiency: AI automates repetitive tasks, boosting human productivity and freeing up time to focus on more complex tasks. 
• Cost Reduction: AI can significantly lower operational costs by optimizing processes and reducing human labor costs.
• Improved Decision-Making: AI can provide high-quality, data-driven insights and forecasting to improve business decision-making. 
• Customer Experience: AI enables personalized interactions in customer service and customized recommendations, improving client satisfaction. 
• Innovation: AI drives innovation by revealing new patterns in data and optimizing workflows.

What does an AI developer do?

AI developers build, train and scale AI models and applications. Some core aspects of an AI developer’s duties include:

• Deep Learning: Expertise in deep learning, which focuses on neural networks and their architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
• Computer Vision: Strong knowledge of computer vision, including image classification, object detection, image segmentation, and image generation.
• Statistical Analysis and Probability Theory: A solid foundation in statistics and probability theory, including hypothesis testing, regression analysis, and statistical distributions.
• Algorithmic Thinking: AI developers need strong algorithmic thinking skills to design and develop efficient algorithms for AI applications.
• Software Engineering and Version Control: Proficiency in software engineering principles, writing clean and maintainable code, and using version control systems like Git.

AI Skills and Qualifications

AI developers will often hold a degree in artificial intelligence, computer science or similar, though this is not essential. When hiring an AI developer, it’s most important to look for strong experience in AI development.

There are also many certifications and qualifications that an AI developer may have. Popular certifications include:

• Google Professional Machine Learning Engineer Certificate
• Google: Generative AI for Developers
• Microsoft Certified: Azure AI Engineer Associate
• IBM AI Engineering Professional Certificate
• AWS Certified AI Practitioner
• AWS Certified Machine Learning – Specialty
• DeepLearning.AI TensorFlow Developer Certificate
• Stanford Artificial Intelligence Graduate Certificate
• United States Artificial Intelligence Institute (USAII): Certified Artificial Intelligence Scientist
• APMG International: Artificial Intelligence Essentials Certificate
• IAPP AI Governance Professional
• Intellipaat’s AI and ML Certification

Beyond technical skills, soft skills are vital when choosing your AI developer. Problem-solving is crucial, aiding with troubleshooting and continuous learning. Communication is also essential when looking for any new team member, particularly in facilitating clear and open communication between technical and non-technical stakeholders.

Popular AI-Related Languages

While there is no single AI programming language, there are some languages that are better suited for AI development:

• JavaScript: Enables machine learning (ML) models to run directly in a browser using libraries like TensorFlow, which is especially useful for creating AI-powered web applications without server-side processing.
• Python: Benefits of using Python for AI include its extensive libraries and frameworks, full documentation, support for multi-paradigm programming and overall versatility.
• Java: As an object-oriented, robust and scalable language, Java is often used in enterprise AI applications.
• C++: Allows for detailed control over system resources, allowing developers to optimize performance at a deeper level than higher-level languages.
• R: Provides powerful, open-source tools for statistical analysis, machine learning, and data visualization, perfect for predictive modeling.

Popular AI Libraries

AI libraries are pre-written code collections providing functionalities for developing AI applications. Popular libraries include:

• PyTorch: An open-source machine learning library developed by Facebook’s AI Research Lab, known for its dynamic computational graph.
• Scikit-Learn: An open-source Python library that offers data mining and machine learning capabilities, with comprehensive documentation and tutorials.
• PyBrain: An open-source machine learning library for Python that provides a simple and flexible environment for experimenting with machine learning algorithms.
• Theano: An open-source numerical computation AI library for Python that played a significant role in early deep learning research.
• OpenCV: A library of programming functions mainly for real-time computer vision, originally developed by Intel.
• NumPy: A Python library that adds support for large, multi-dimensional arrays and matrices, popularly used for machine learning.
• Pandas: An open-source Python library commonly used in data science and machine learning.
• Detectron2: A flexible library developed by Facebook’s AI lab for advanced object detection tasks in computer vision.

Popular AI Frameworks

Frameworks are collections of reusable code, templates and tools that provide a foundational structure for building applications effectively and efficiently. Some of the most popular AI frameworks include:

• TensorFlow: An open-source deep learning framework developed by Google, renowned for its flexibility and scalability and well suited to AI applications.
• LangChain: Allows developers to build applications using LLMs with features like model I/O, data connections, agents and callbacks, and integrating easily with ChatGPT and Hugging Face Transformers.
• DL4J: Short for Deeplearning4j, this provides a comprehensive set of tools for building and deploying deep neural networks in Java-based applications.
• Microsoft Cognitive Toolkit: A deprecated deep learning framework developed by Microsoft Research that’s both powerful and scalable.
• MXNet: An open-source deep learning framework known for its efficiency and scalability, commonly used for both research and production.
• Caffe: An open-source deep learning framework known for its speed and efficiency in computer vision and able to support a variety of deep learning architectures.
• XGBoost: An open-source gradient boosting framework known for its efficiency and performance.
• LlamaIndex: An open-source framework that integrates LLMs with external, real-world data sources.

Popular AI ​​Programming Tools

There are many tools available to enhance the experience of developing AI. Some of the most popular tools include:

• IBM Watson: A suite of AI and ML services that offers tools for building AI-powered applications, including natural language processing (NLP) and computer vision.
• OpenAI: A range of tools for different AI tasks, including making images or converting text to speech.
• Hugging Face: A suite of AI tools mainly known for their Transformers library, which helps with machine learning tasks like language processing and creating chatbots.
• Keras: An open-source, high-level neural network API that runs on top of TensorFlow or other frameworks, ideal for quick prototyping.
• OpenNN: A tool used for creating neural networks, written in C++ and known for being fast and efficient.

How do engineers address bias in AI models?

An AI engineer will typically use two main methods to combat bias in AI models. First, they assess bias in the sampling and pre-processing stage, ensuring the data is varied and fully representative. They will also conduct audits to identify and negate biases that might arise over time.

How much does it cost to hire an AI engineer?

When hiring traditionally in the US for a local, mid-level AI developer, the average annual compensation can be upwards of $200,000. However, even by just expanding to other parts of the United States, salaries can be reduced significantly.

On average, companies hiring AI developers with HireDevelopers can expect to save between 50% and 80% on salaries and hiring costs, with exact salaries varying based on location and experience. The greatest savings can be found by hiring in Africa and Asia, while companies looking for aligned time zones and a closer cultural match can hire from our extensive pool of Latin American developers.

Is AI in demand in 2025?

AI is a massively in-demand skill in 2025. GitHub, for example, shows 248% year-over-year growth in generative AI projects on the platform, and companies across every industry are rapidly adopting artificial intelligence.

If you’re struggling to find top AI developers for your project, consider using a platform like HireDevelopers, which offers thousands of pre-vetted AI developers for 50% to 80% less than a local hire.

Why is it hard to find good AI developers?

It can be hard to find great Python developers due to the high demand for Python engineers in the United States. Recently, PartnerStack placed AI Engineers at the top of their list of most in-demand professions in 2024, a trend which is continuing into 2025.

AI is being adopted by leading companies across every industry, including Meta, Google, Microsoft and Amazon.

If you’re struggling to find elite AI developers, try HireDevelopers today to tap into our vast network of senior, pre-vetted Latin American AI developers in just 24 hours.

How to hire AI developers

The first step to hiring an AI developer is to decide on a hiring method. Traditional hiring methods include local job adverts, LinkedIn job posts and using your professional network to identify potential candidates.

However, these methods are often time consuming and can be expensive. The median time to hire a developer is around 41 days, with the slowest 10% taking up to 82 days and 38% of companies reporting project delays due to recruitment problems, according to the Linux Foundation’s 2024 State of Tech Talent Report.

Alternatively, many companies are now turning to services like HireDevelopers to cut costs and speed up the hiring process. With HireDevelopers, you can hire AI developers from anywhere in the world, at any price point, in just 24 hours.

When putting together a job description or job interview for an AI developer, prioritize technical skills, like familiarity with key languages such as Python, Java and R.

You should also consider using coding challenges alongside a traditional interview during the hiring process – if you’re using a pre-vetted talent pool like HireDevelopers, this has already been done for you. Coding challenges allow you to assess problem-solving skills and coding expertise in a realistic, real-time environment.

If you need help putting together an AI job description or conducting an AI job interview, you can check out our helpful and comprehensive guides below. Alternatively, to cut your time-to-hire by 90% and unlock savings of up to 80%, get in touch and book your free HireDevelopers consultation today.

Why hire AI developers with HireDevelopers?

Hiring the right AI developer for your team is a huge undertaking, and the process can be intimidating, especially for startups and small businesses.

At HireDevelopers, our pre-vetted, global tech talent marketplace streamlines your hiring journey and cuts time-to-hire by over 90% without compromising on quality or stretching your budget. Receive a custom shortlist of top AI developers at any seniority level, at any price point and located anywhere in the world within just 24 hours of your consultation.

Plus, we’ll handle all payroll, legal compliance and HR completely for free, giving you more time to start building with your new AI dev.

Get in touch today and start hiring top-notch AI developers in just 24 hours.