Artificial intelligence, including generative AI (GenAI), is evolving at breakneck speed, creating unprecedented opportunities, and challenges, across every industry and function. Although the AI revolution is still in its early stages, the sophistication and quality of AI output is improving literally by the week. As technology advances, the gap between leaders and laggards will only widen; and at the speed of change we’re witnessing, that gap may—sooner rather than later—become unbridgeable.
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Contact Us Today!The time to act is now. Companies looking to maximize the benefits of AI in business and their value potential should be experimenting with AI as early as possible, deepening their commitments by automating processes, enhancing decision-making, forecasting demands, and offering personalized experiences. Business leaders must focus on turning the promise of predictive and generative AI into real, sustained value.
However, around 70% of all AI transformations fall short of expectations, with executives citing the key challenge as lack of AI expertise. The shortage of AI skills is projected to take a large toll on businesses. Research from the International Data Corporation predicts that, by 2026, 90% of businesses will have felt the impact of the AI skills shortage—to the tune of some $5.5 trillion in lost revenue.
For this reason, the role of the AI Developer has been in the spotlight, with the talent gap more than apparent. Finding highly skilled AI developers can be a real pain. The demand for AI developers currently is outpacing supply highlighting the pressing need for proven approaches to finding top-tier AI experts who can get the most out of AI-based services such as data analytics, predictive analytics, image processing, speech recognition, and chatbots.
But worry not! In this guide, you will discover what it takes to hire the best AI developers who can deliver quantum leaps in productivity within your organization. We’ll cover the key skills you need in AI developers, explore different hiring models, ideas for interview questions, salary insights, and offer tips for optimizing the hiring process to find the right talent for the role.
What is an AI Developer?
Also goes by: AI Engineer, Machine Learning Engineer, Data Scientist, AI Research Scientist, Machine Learning Scientist, Deep Learning Engineer, Natural Language Processing Engineer, or Computer Vision Engineer.
AI developers are versatile experts who use their skills in data analytics, machine learning, programming, and other technologies to design, build, and implement intelligent applications that can process vast amounts of data, learn from patterns, predict trends, and make informed decisions. This includes intelligent assistants that mimic human cognitive abilities and automate complex tasks such as chatbots for instant customer support, recommendation systems for personalized content, and even autonomous vehicles.
The technology components used by AI developers include machine learning (ML), deep learning, generative AI (GenAI), natural language processing (NLP), text analytics, rich media analytics, tagging, searching, categorization, clustering, hypothesis generation, question answering, visualization, filtering, alerting, and navigation.
AI development teams on the other hand typically consist of individuals with a diverse skillset and experience, such as technical product management, UI/UX design, advanced mathematics, software engineering, and data-specific AI expertise with an in-depth knowledge of the industry and the business unit.
Potential AI Developer Roles Within an AI Team
Why Hire AI Developers
AI is everywhere around us, and companies across all industry verticals are clamouring to hire the best AI developers. An AI developer/ AI engineer can help turn data into actionable insights, create intelligent applications, automate processes, enhance customer experiences, and streamline operations. Without the right AI talent, your business risks falling behind in today’s AI-driven world. To realize the full benefits of AI, businesses need to hire AI developers. As companies identify ways to use AI to achieve strategic goals, the role of AI developers is one that’s likely to remain in high demand for years to come.
What Roles do AI Developers Play in Various Industries
AI developers are spearheading applied AI innovation across various industries, with cutting-edge applications that promote human-machine collaboration.
For companies looking to automate processes or generate content, GenAI is useful, but it only scratches the surface of AI’s capabilities. AI’s greatest value lies in core business processes such as operations, sales, marketing, and R&D where leaders are generating 60% of the value. Contrary to popular belief, support functions contribute less value—40% of the value—with customer service, IT, and procurement being the top contributors. Leveraging AI in both core business and support functions can give your business a competitive advantage.
Let’s explore the critical contributions of AI developers in healthcare, financial, automotive, and energy sectors.
Applications of Artificial Intelligence (AI) in Healthcare
In healthcare, AI developers are revolutionizing the sector by creating medical imaging systems for accurate diagnoses, predictive models for patient outcomes, and virtual assistants to enhance patient care.
Imagine a doctor being able to accurately diagnose medical images like X-rays and MRIs with the help of AI algorithms. This has become a reality because AI developers have created AI-powered systems that can analyze these images to diagnose diseases like cancer at an early stage, improving the chances of successful treatment.
Healthcare companies seeking advanced solutions are hiring AI developers to lead these innovations.
Applications of Artificial Intelligence (AI) in Financial Services
Financial institutions aiming to expand their services are hiring AI developers with expertise in algorithm development and predictive analytics to develop various solutions. For example, AI developers at PwC have created an AI and machine learning-backed bot that detects anomalies in ledgers. Its algorithms are trained to replicate the thinking and decision-making of an expert auditor. It inspects the uploaded transactions, and corresponding amounts to detect potential errors and fraud in the ledger.
Investment banks such as JPMorgan Chase, Goldman Sachs, and Morgan Stanley are integrating AI into trading algorithms, risk assessment, and systems offering personalized investment recommendations.
Insurers are focusing on operations (policy administration, underwriting, and claims management), customer service, and marketing and sales. Predictive AI algorithms have been employed in the areas of scoring, fraud assessment, triage and policy automation.
The adoption of GenAI across the financial sector is strongest in the use of chatbots to resolve questions and summarize customer interactions. Other solutions that have been implemented include automated tender assistants, RFP response automation tools, and HR service chatbots—all designed to address specific challenges and deliver quantifiable outcomes.
Check out our comprehensive guide on How to Hire Nearshore Fintech AI Developers from Latin America.
Applications of Artificial Intelligence (AI) in the Automotive Industry
For automotive and mobility companies, unlocking the full potential of traditional and generative artificial intelligence (GenAI)—particularly in driving cost and efficiency—has become a strategic, even existential, challenge. From manufacturers and suppliers to dealers and service providers, players in the automotive and trucking industry have shown an urgency to leverage AI and GenAI to empower teams, reimagine processes, and strategically align technology implementation with value and cost-focused P&L objectives.
Companies like Tesla, General Motors, and Toyota, are using the vast amounts of data produced by electric and other sophisticated modern vehicles to transform the driving experience (autonomous driving being one example) and optimize the vehicle life cycle (such as through improved design and predictive maintenance).
In production lines, AI developers are innovating predictive maintenance systems, automation robots, and quality control algorithms in manufacturing. To improve production processes, automotive companies are hiring AI developers skilled in reinforcement learning and data analytics.
Applications of Artificial Intelligence (AI) in the Energy Sector
Many energy companies have adopted AI tools to support various tasks. For example, since the arrival of generative AI (GenAI), many oil and gas companies have adopted third-party office productivity tools, such as solutions using “retrieval augmented generation” techniques (for example, Microsoft’s Copilot), to support tasks like corporate report writing.
Beyond office applications, oil and gas companies are hiring AI developers to develop solutions that improve the quality and productivity of oil field operations and petro-tech disciplines. For example, they are using AI-backed software to support asset maintenance, such as by analyzing corrosion images and generating predictive alerts to help prevent issues before they arise.
AI developers are also developing chatbot-like applications that offer real-time technical troubleshooting guidance to frontline technicians. In addition to streamlining daily operations, companies are investing in AI to support long-term objectives. For example, they are testing how AI can assist in advanced scientific analysis to improve the efficiencies of electrolyzers and carbon capture materials to support the use of hydrogen, geothermal, and carbon capture sequestration (CCS) to lower emissions.
Future benefits will include developing AI applications that promote major innovations, boost performance by improving end-to-end operations, reduce costs, and manage the energy transition. The ultimate vision is to enable autonomous operations, including synthetic seismic data evaluations, AI-generated well designs, and AI-guided operations.
12 Essential Skills to Look for in an AI Developer
AI developers are in high demand and it suddenly seems like everybody wants to be an AI developer. Yet only a few select individuals actually have the abilities necessary to design, develop, and implement enterprise-level AI models and systems.
These days, finding the right mix of technology skills for AI teams is challenging. Hiring managers cannot expect to find an abundance of people with 20 years experience in machine learning. The technologies are too novel. When hiring AI developers, most companies are looking for individuals with a four-year degree in math, data science, statistics or computer science to qualify for an entry-level AI developer position. Masters and doctorate degrees in computer or cognitive science are becoming increasingly common for workers specializing in AI development as well as desired by the companies who are hiring them.
In addition to educational degrees, among the skills and knowledge those working as AI developers must have are:
1. Programming Proficiency
It goes without saying that an AI developer needs to be proficient in programming languages. Some of the commonly used languages for AI development include:
Python
Python is especially popular due to its easy-to-learn syntax and extensive libraries for AI and machine learning. Knowledge of these languages is crucial for developing algorithms, implementing models, and working with data.
Libraries such as TensorFlow,
PyTorch, and Keras are commonly used in Python for AI development. Hugging Face’s Transformers library has also gained popularity for integrating generative models like GPT-4 and LLaMA. Use cases include predictive analytics, natural language processing, and image recognition.
R
The R language is primarily used for statistical analysis and graphical representation. The ‘caret‘ package in R aids in predictive modeling, while ‘e1071‘ simplifies the process of creating a machine learning model. R finds its use in AI projects related to scientific research, healthcare, finance, and social media analytics.
Java
Java is another popular choice in AI development, mainly due to its simplicity, readability, and excellent user interaction capabilities. Its strong memory management and the breadth of high-quality machine learning libraries, such as Stanford Library NLP and Apache Open NLP, make it suitable for chatbot development.
Use cases include chatbots, website recommendation systems, and fraud detection in the finance sector.
C++
C++ provides the ability to execute high-level applications with a relatively low computational cost, making it suitable for machine learning and neural network computation.
Libraries like Shark and mlpack are designed for machine learning in C++. AI use cases include computer vision tasks, robotics, and gaming.
Julia
Julia is gaining traction in machine learning due to its unique blend of speed, ease of use, and powerful features. Julia’s speed often rivals or even surpasses the performance of languages like C and C++. This is crucial for computationally intensive machine learning tasks, allowing for faster model training and experimentation.
2. Strong Systems Knowledge
Ensuring that an AI developer has solid foundational knowledge of AI systems and the frameworks required to build them is key. AI development is different from traditional software development, and specialized skills are required to reap the maximum benefits. With current technology it’s too easy to trust a model and its results. This is a danger that needs to be overcome with statistical methods and a healthy skepticism. The combination of programming knowledge and a heightened awareness of unconscious biases is crucial for AI developers, especially in today’s context. The age-old adage “garbage In, garbage out” still holds true, albeit in a more nuanced manner, and it’s necessary to implement guardrails to mitigate unintended outcomes. Hiring developers with systems knowledge will not only create greater efficiencies, but also help secure organizational data.
3. Linear Algebra, Probability and Statistics Mastery
A strong foundation in advanced mathematics is important for AI developers. This includes a deep understanding of linear algebra, probability, and statistics, which are fundamental to the development and analysis of AI algorithms.
AI developers should possess the ability to develop custom algorithms based on these mathematical and machine learning principles to address unique and challenging problems.
4. Solid Database Modelling, Data Warehousing and Data Processing Skills
Employers around the world are searching for Artificial Intelligence experts who have a broad computer science skill set. Ever since the “big data” movement got underway several years ago, many IT leaders have observed that most of the work involves data collection and cleaning. AI doesn’t change that view. Data is the foundation of AI, and AI developers need to have a deep understanding of data modeling and management.
It’s not a matter of taking a foundation model that has all of the world’s knowledge built-in and setting that application loose on the problem, rather AI developers will need to collect data for fine tuning. An AI developer has to understand what that data means and what kinds of biases are built into the data.
They should know how to acquire, clean, and transform data into a suitable format for analysis. Knowledge of SQL and NoSQL databases is essential for querying and managing large datasets. Experience with tools such as Apache Spark, Hadoop, and AWS S3 that are also commonly used for data processing in AI projects is also important.
5. Big Data Analysis
AI developers must possess strong big data analysis skills. This involves the ability to effectively analyze and extract meaningful insights from massive datasets. Proficiency in big data technologies such as SparkSQL, Apache Flink, Apache Arrow, and BigQuery is crucial for querying, manipulating, and processing large-scale datasets efficiently. This expertise allows AI developers to build robust and scalable AI models that can handle the increasing volume and complexity of data generated in today’s world.
6. Knowledge of Machine Learning Models
Knowledge of machine learning models and algorithms is a must for AI developers. Put simply, Artificial Intelligence (AI) enables machines to carry out tasks in a way that we consider ‘smart’ and machine learning is the method AI developers use to make this a reality, without explicitly telling the machines what to do.
This includes understanding supervised and unsupervised machine learning techniques, as well as deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
AI developers should also know how to evaluate the performance of these models using metrics like
- Accuracy
- Precision
- Recall
- Root-mean-square deviation (RMSE),
- Mean absolute error (MAE)
- F1 score
7. Familiarity with Neural Network Architectures
Understanding and applying neural networks in AI solutions is a critical skill for AI developers.
This involves knowing the different types of neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), their applications, and how to implement them.
Some tools used for developing neural network architectures include:
- TensorFlow: an open-source library for building and training neural networks
- Keras: a high-level API that runs on top of TensorFlow to simplify the process of building, testing, and deploying deep learning models
8. Familiarity with Generative AI Models and Reinforcement Learning
AI developers should possess a strong understanding of cutting-edge AI techniques. This includes familiarity with generative AI models such as GPT-4, Stable Diffusion, and LLaMA, which are revolutionizing fields like natural language processing and image generation. Furthermore, a solid grasp of reinforcement learning is essential. This crucial area of AI enables the development of intelligent agents that learn through interactions with their environment, finding applications in robotics, game AI, and beyond.
9. Knowledge of AI and ML Cloud Services
AI developers should also be familiar with cloud-based AI and machine learning services offered by major providers such as AWS, Azure, and Google Cloud Platform. AWS’s Bedrock now offers managed foundational models, and Google’s Vertex AI has added features simplifying the deployment of large language models. These services provide pre-built models, APIs, and tools for developing intelligent applications without the need for extensive coding. Additionally, they should have some knowledge of low-code/no-code AI platforms like Microsoft Power Platform and Google AutoML which have made AI development more accessible to those without extensive programming backgrounds.
10. Domain Knowledge
Having domain knowledge in a specific area of focus can give an edge to an AI developer.
For example, if you’re working on healthcare-related AI projects, hiring AI developers with a strong understanding of the healthcare industry and its challenges can help you develop more effective solutions.
Similarly, if you work on financial AI projects, hiring developers with a background in finance or economics can be beneficial.
Non-Technical Workplace Skills for AI Developers
Apart from technical skills, AI developers should also possess certain soft skills to excel in their roles. These include:
- Verbal and written communication
- Effective collaboration
- Problem-solving
- Creativity
- Critical thinking
11. Strong Verbal and Written Communication Abilities
Beyond technical skills, a candidate should be able to explain concepts and results to business stakeholders in clear and simple terms. AI explainability poses a major challenge in the industry. Business leaders shouldn’t feel confused or misled by cryptic terms or jargon.
12. Effective Collaboration
Moreover, as AI projects are often team efforts, collaboration is essential for efficient project execution.
AI developers are often in constant meetings with other techies, such as:
- Data scientists, to discuss model development and optimization
- Data analysts, to understand data requirements for AI projects
- Software developers, for integration of AI models into existing systems
- Project managers, to ensure timely and successful project delivery.
Therefore, effective collaboration skills are important.
12. Critical Thinking and Problem-solving
AI and computer science more broadly can be seen as a way of solving problems. From small ones such as cleaning data to feed into an AI, to developing sophisticated algorithms, and troubleshooting issues that arise during model development or deployment. Having an analytical mind and the ability to think critically and solve complex problems is essential for AI developers. Therefore, instead of specific Python experience, start by identifying people with solid problem-solving skills, the ability to write code and learn new things quickly.
How to Hire an AI Developer
Acquiring all the skills outlined in the previous section doesn’t happen overnight. It takes years of training. In a rapidly evolving field, it’s no wonder that the time needed to acquire such skills contributes to the talent shortage. There just isn’t enough top AI developers to hire. It’s very hard to go from zero to AI guru in a matter of months and companies can’t train their way out of the talent shortage alone.
Today, advanced AI development teams are made up of a mix of new hires, current employees, business and technical experts, contract workers, and those from diverse backgrounds. Companies will benefit if they can identify the right capabilities with which to build AI teams versus just the skills around certain technologies or degrees.
There are 3 common Models for Hiring AI Developers:
- Full-Time Staff
- Independent Contractors
- Collaborate with Vendors that Specialize in Providing On-Demand Teams of AI Developers
Developing solutions exclusively in-house, without the help of partners will limit potential benefits and slow progress toward your AI goals.
Organizations that use a combination of internal and third-party teams (Hybrid Model) to develop and deploy Predictive AI and GenAI solutions typically yield greater satisfaction, productivity, and cost savings compared to those that try to go it alone. Two levers are key to such collaboration:
- Working with vendors on AI initiatives typically results in a clearer deployment and better ROI tracking, as well as more cost savings.
- Overall, organizations have more success with outcome-based commercial models for AI vendors than with fixed-price models.
Therefore, vendor collaboration yields greater satisfaction, ROI clarity, and cost savings.
When collaborating with a third-party vendor, use an outcome-based model. In our experience, organizations that use an outcome-based commercial model for AI development vendors are more likely to report cost savings compared to those that use a fixed-price model.
How Much Does it Cost to Hire AI Developers in 2025
Although the US Bureau of Labor Statistics (BLS) doesn’t offer data specifically for AI developers, it projects job growth in all computer and information technology careers to grow at an above-average pace in the decade leading up to 2032. BLS projects a 25 percent growth between 2022 and 2032 for similar jobs, like software developers.
In addition to solid job growth, AI developers enjoy higher earning potential. In 2025, hiring an AI developer can vary widely in cost, depending on factors like their expertise, location, and the complexity of the project. On average, you might expect to pay between $80,000 and $150,000 annually for a full-time AI developer in the United States. For freelancers or contract workers, rates can range from $50 to $200 per hour, influenced by their level of experience and the specifics of the job.
Factors Influencing the Cost of Hiring an AI Developer
1. Location
The cost of hiring AI developers varies significantly based on geography. The developers’ geographical location plays a significant role in cost variation, as rates differ across regions. This is because living costs and local demand for technical talent greatly affect salaries.
- United States: AI developers in the U.S. command some of the highest salaries globally, with annual salaries ranging from $100,000 to $150,000 for mid-level developers. Senior-level AI developers, particularly those in high-demand sectors like finance or healthcare, can earn $200,000 or more per year. Additionally, developers working in tech hubs like San Francisco or New York can expect even higher compensation due to the intense demand for top talent in these areas.
- Latin America: AI developers in Latin America earn significantly less, with salaries typically ranging between $20,000 and $50,000 per year. This lower cost makes Latin America a popular destination for companies looking to outsource AI development. Despite lower wages, nearshore AI developers in Latin America are known for strong technical skills, particularly in machine learning and data science, making it a cost-effective option for businesses with limited budgets.
- Europe: Salaries in Europe vary widely depending on the country. In Western Europe, particularly in countries like the U.K. or Germany, AI developer salaries can range from $60,000 to $120,000 annually. Meanwhile, developers in Eastern European countries such as Poland, Ukraine, or Romania can expect salaries between $30,000 to $70,000 per year, making these regions attractive for outsourcing AI talent at a lower cost than in Western Europe.
2. Complexity of the Project and Experience Level Required
AI projects can range in complexity, with different tech stacks and features required for various applications. For example, developing a machine learning model for financial forecasting demands different tools and expertise than creating an NLP system for customer service chatbots.
Each type of AI application has unique requirements, which directly impact the cost of hiring an AI developer. The expertise and experience of the AI developers are also crucial in determining cost. Hiring highly skilled developers with a strong track record can be more expensive, but their experience often leads to higher quality work and innovative solutions.
Specialization in specific AI subfields can further impact salary expectations. Developers with expertise in cutting-edge areas such as:
- Natural Language Processing (NLP)
- Computer Vision
- Reinforcement Learning
These technologies are in high demand, particularly in industries like finance, autonomous driving, and healthcare, and developers with mastery of frameworks such as TensorFlow, PyTorch, or languages like Python and Julia can negotiate higher compensation. For instance, a senior AI engineer specializing in NLP or computer vision may command a salary close to or exceeding $300,000 annually due to the rarity and high demand for such skills.
Wrapping Up
Since the talent shortage is so intense, AI developers are going to come from several sources. There just isn’t enough top AI talent to hire. Advanced AI teams will need to be a mix of new hires, current employees, business and technical experts, contract workers, and those from diverse backgrounds. Companies will benefit if they can identify the right capabilities with which to build AI teams versus just the skills around certain technologies or degrees. For example, instead of specific Python experience, start identifying people with solid problem-solving skills, the ability to write code and learn new things quickly.
It’s not only about who, but how AI teams work within an organization that will matter. Advanced AI teams are going to have to be thoughtfully structured, so the work is attractive and top performers have incentives to stay.
Hire AI Developers with Next Idea Tech
At Next Idea Tech, we are harnessing our far-reaching expertise in AI to help clients across the world unlock AI-driven value creation. This year, we have helped companies leverage AI in a way that is explainable, secure, and robust to transform their businesses at scale and gain new competitive advantage. We help clients realise the full power of AI responsibly—mitigating risks, maintaining data security, and supporting the trustworthiness of AI outputs.
With a dedicated team of AI experts and a proven track record of delivering numerous successful projects, we are a leading choice for AI development services worldwide. Our extensive experience spans across various industries, delivering AI-powered solutions that drive efficiency and innovation.
Whether you’re looking to build a sophisticated AI platform from the ground up or scale an existing one with cutting-edge features, Next Idea Tech is your trusted tech partner. We’re here to turn your vision into a seamless reality. Your search for skilled AI developers ends at Next Idea Tech. Contact us today, and let’s start building your AI-driven future.
