In 2026, companies continue to hire AI talent to stay competitive. Job postings requiring AI skills in the US are up 143% year over year. At the same time, 1 in 2 CEOs plan to prioritize AI skills in their workflows as a top investment for the next three years, so the demand for qualified AI engineers shows no signs of slowing down.
But what skills does an AI developer need, and what should you look for if you plan to hire one for your business? In this article, you’ll find out the 13 essential and in-demand AI developer skills in 2026.
In this section, we’ll take a detailed look at the foundational competencies every AI developer must have.
Keep in mind, your candidate doesn’t need AI expertise in every language or framework named here. A skilled AI developer should have a comprehensive understanding of these areas but work with the technologies relevant to their specific domain.
Let’s start with the basics: any AI developer must have a solid foundation in programming.
While you can use many languages for AI, Python has been the industry standard for years due to its vast number of pre-built libraries available. In fact, most AI development happens in Python.
However, proficiency in other languages indicates versatility and a deeper understanding of software engineering principles. For example, many AI developer skills include proficiency with:
When hiring, you should look for solid Python programming skills alongside familiarity with at least one other relevant language.
Here is a more detailed table of the main characteristics of languages used in AI and the skills needed for each:
Python
Simple syntax, extensive libraries (TensorFlow, PyTorch, Scikit-learn, Keras), a large active community, and broad industry adoption. The go-to language for AI.
Writing clean, efficient code for data analysis, model training, AI app development, and rapid experimentation.
R
Primarily used for statistical analysis and data visualization. Popular in academia and research.
Performing complex statistical modeling and data visualizations.
Julia
High-performance language designed for numerical computing. Strong in scientific and mathematical workloads.
Running large-scale simulations and handling performance-critical AI tasks.
Java
Known for its scalability and performance. Common in large enterprise systems.
Integrating AI models into existing large-scale Java-based applications.
C++
Offers high performance and low-level memory control, ideal for speed-critical applications.
Optimizing AI algorithms for performance, especially in computer vision.
JavaScript
Runs in browsers and supports AI in front-end applications.
Embedding AI features into web applications, using JS-based frameworks.
An AI model is only as good as the data it’s trained on. This is why the ability to handle data is one of the core AI engineer skills.
Before an AI developer can even think about building a model, they must source, clean, structure, and validate vast amounts of data. This process, often referred to as preprocessing, ensures that the AI learns from accurate information.
In practice, it means your candidate must be proficient with databases and big data tools. They need to write queries to pull data from SQL or NoSQL databases and use tools like Apache Spark or Hadoop to process large datasets.
Think of it this way: if programming is the engine, data is the fuel. Without strong data skills, the AI developer can’t get the project off the ground. These skills often overlap with those of a data engineer, which is a related but distinct role. If you want to know more about data expertise, read our data engineer skills guide.
You don’t need to become a machine learning expert to hire one, but you do need to understand what type of problem-solver you are looking for.
AI developers work with different types of machine learning models to solve specific business problems. You should look for experience with:
An AI developer should not only be able to choose the right model for the job but also evaluate its performance. Thus, they need to be familiar with a range of evaluation metrics, such as accuracy, precision, recall, and F1 score.
Explore the DOIT guide on machine learning skills to understand the full set of capabilities your team might need.
Moving forward, the next fundamental part of AI developer skills is familiarity with AI frameworks and libraries. These are collections of pre-written code and tools that accelerate everyday AI development tasks.
Your AI specialist may work with a range of different frameworks, depending on the use case and domain. For example, TensorFlow and PyTorch are go-to choices for building complex neural networks, while Scikit-Learn is excellent for data analysis tasks.
Here’s an overview of popular frameworks and libraries for modern artificial intelligence skills.
TensorFlow
Google’s comprehensive ecosystem for building and deploying ML models, especially for large-scale applications.
Computer vision, natural language processing, predictive analytics, and recommender systems.
PyTorch
Meta’s deep learning framework, popular in research and rapid prototyping for its flexibility and ease of use.
Deep learning research, building custom neural networks, NLP tasks, and reinforcement learning experiments.
Scikit-Learn
A user-friendly library built on top of other Python libraries. It provides simple tools for data mining and analysis.
Classification, regression, clustering, and other traditional ML tasks.
Keras
A high-level neural network API running on top of TensorFlow. It allows AI developers to quickly prototype and test deep learning models.
Rapid prototyping, image recognition models, simple deep learning workflows, and classroom demos.
XGBoost
A gradient boosting framework, especially strong with structured/tabular data.
Fraud detection, recommendation systems.
Hugging Face
A platform and library providing thousands of pre-trained models, primarily for NLP tasks. It simplifies using current frontier NLP models.
Building chatbots, text summarizers, sentiment analysis tools, and translation pipelines.
spaCy
An advanced NLP library strong at processing large text volumes.
Named entity recognition, text classification, information extraction, and dependency parsing.
NLTK
One of the oldest NLP libraries, widely used in academia. Provides tools for linguistic analysis and text preprocessing.
Tokenization, stemming, part-of-speech tagging, and text preprocessing.
OpenCV
An open-source library focused on real-time computer vision applications.
Image recognition, object detection in videos, facial recognition systems, and document scanning.
The AI developer must understand how to use these libraries and, more importantly, when to combine them. For example, they can use PyTorch for custom training, along with Hugging Face models, to accelerate LLM deployment.
Today, most AI development happens in the cloud. Major providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), offer powerful AI/ML services that handle the underlying infrastructure.
Your choice of a cloud provider might depend on your company’s existing infrastructure. Therefore, it is advisable to hire an AI developer who is familiar with your chosen ecosystem.
They should know how to use its services for model training and deploying pre-built APIs for computer vision, natural language processing, or other tasks.
ML platform
SageMaker
Azure ML
Vertex AI
Computer vision
Rekognition
Azure AI Vision
Vision AI
NLP
Amazon Comprehend
Azure AI Services
Natural Language API
Generative AI
Amazon Bedrock
Azure AI Foundry
Gemini
Data analysis
AWS Glue, Redshift
Azure Synapse Analytics
BigQuery, Dataflow, Looker Studio
Building a working AI model is only half the work; getting it into the hands of users is what delivers business value. This “last mile” is called deployment, and those are AI developer skills that separate experts from beginners.
A skilled AI developer knows how to package an AI application using Docker so it can run reliably in any environment. They also configure the necessary computing resources, like CPU and memory.
But the job doesn’t end after deployment. AI systems can degrade over time as new data comes in. That’s why an AI developer should be familiar with these monitoring techniques:
Reliability matters more in 2026 than it did even a year ago. The 2025 Stack Overflow Developer Survey found that 66% of developers spend more time fixing AI-generated code. That puts new weight on the techniques above. Evals (automated tests that grade an AI’s output for accuracy), guardrails, observability, and tool-use checks all belong on the basic AI developer hiring checklist.
The AI development market underwent a significant shift with the public release of generative models. And it created a demand for a new set of AI developer skills focused on customizing and building applications on top of these large foundation models. In 2026, that list now covers tool use, AI agents, MCPs (Model Context Protocol), and the broader agentic AI stack that lets models act on the outside world. Let’s take a look.
Not long ago, building a useful AI model required a team of researchers and months of training. Today, AI developers can use pre-trained generative AI models via an API and integrate them into business applications.
In 2026, nearly every AI developer job description includes hands-on experience with at least a few of the following models:
An AI developer must understand the capabilities and limitations of different models and how to interact with their APIs. And most importantly, they should know how to choose the right one for your specific business workflows.
Now let’s focus on how AI developers stitch these generative AI models into usable products.
An AI application framework is a collection of libraries and tools that provides the structure to connect LLMs with data sources, APIs, internal services, and user interfaces. These frameworks provide a structured environment for data integration, retrieval, orchestration, and validation.
Here are the leading AI app-building frameworks in use among AI developers in 2026:
For more on workflow orchestration, see the DOIT article “n8n vs Make vs Zapier: Which is Best for Workflow Automation?“
Off-the-shelf generative AI models are powerful, but they don’t know the specifics of your business. To get real value, you need AI developer skills that can customize these systems. There are several ways to do this, ranging from simple to complex.
The most straightforward method is prompt engineering. This is the skill of crafting precise instructions to guide the AI’s response. A skilled prompt engineer can significantly improve a model’s performance without any technical changes, simply by refining the questions you ask it.
They also use few-shot learning, a technique where they provide the model with a few high-quality examples to show it what a good output looks like.
For stronger customization, AI developers use two primary approaches:
This technique gives the AI model secure access to your company’s private information (for example, internal documents, knowledge bases, or databases). When a user asks a question, the system first retrieves relevant information from your data and then uses the AI to generate an answer based on those facts.
In 2026, most production RAG systems combine two kinds of search: one that understands meaning, and one that matches exact keywords. A second pass then re-ranks the combined results before sending the best matches to the AI. At the same time, today’s AI models can handle much more text at once than they could a year ago, so a candidate who knows when NOT to use RAG matters as much as one who knows how to build it.
Fine-tuning adjusts the model’s internal parameters to make it an expert in a specific domain. For instance, you could fine-tune a model on your company’s customer support chats to understand the specific terminology related to your product.
Modern AI developers use different fine-tuning methods, most often LoRA / QLoRA (parameter-efficient), SFT (supervised fine-tuning), DPO (direct preference optimization), and RLHF (reinforcement learning from human feedback).
The fastest-growing way to customize an AI model in 2026 is not to retrain it at all. Instead, an AI developer can give the model tools it can call: a function that looks up a customer record. Or one that queries a team calendar. Or one that runs a SQL query against the data warehouse.
The model decides when to call each tool, what arguments to pass, how to handle errors, and how to combine the results. Developers call this pattern tool use, or function calling, and every major LLM supports it today.
Model Context Protocol (MCP) is the 2026 open standard for this kind of plumbing. MCP defines a uniform way for an AI agent to find and call external tools and data sources, so a developer does not have to write custom code for every integration. Anthropic introduced MCP in late 2024 and donated it to the Linux Foundation’s Agentic AI Foundation in December 2025. Every major provider now supports the standard, including OpenAI, Microsoft, Google, and Anthropic itself.
So if you’re using RAG to give your AI access to company data, where exactly does that data live, and how does the AI search it so quickly? That’s the job of a vector database.
When you hire an AI developer, their understanding of these databases is directly linked to their ability to build high-performing RAG systems.
In simple terms, a vector database stores information in a way that allows AI models to understand and compare it based on its contextual meaning.
For example, an AI developer can store your entire product documentation in a vector database. When a user asks a question, the system can quickly find the most relevant sections of the documentation and feed them to an LLM to generate a precise answer.
The most popular vector databases AI developers work with are:
AI developer skills in vector databases are a clear sign that a candidate knows how to build sophisticated, context-aware AI applications.
Beyond technical ability, a great AI engineer needs to be a problem-solver and a strong communicator. Here are the soft AI developer skills that turn a good technician into a great team member:
The best AI developers possess a high level of critical thinking. They can break down complex problems and think through solutions, even when there’s no clear path forward. They are also adaptable and committed to continuous learning, which is essential in a field that is constantly evolving.
The reality of an AI project is that it often involves people from different departments, including business leaders and domain experts. An effective AI developer can explain complex technical concepts in plain English. If you hire a remote AI engineer, ensure that their language skills are at least a B2 (Upper-Intermediate) level for effective communication.
As AI systems become more integrated into our lives, the ethics behind them are more important than ever. A good AI developer understands how to build systems that are fair, transparent, and unbiased. They think about the potential impact of their work and ensure that the solutions they create are both practical and responsible.
Now you have a clear picture of the AI developer skills you may need. The next step is to verify that a candidate has them.
You can start by asking them to discuss their past projects and portfolio. A developer’s portfolio, or relevant links on GitHub, gives you a practical look at their skills and how they have applied them in real projects. Ask them to walk you through code samples, explain the problem they solved, the tools they used, and the challenges they faced.
Next, check for relevant certifications and online courses. While not a replacement for experience, the certifications can show a candidate’s commitment to learning. For example, you can look for:
Finally, conduct a technical assessment. Give the candidate a well-defined take-home project that reflects a challenge your business faces. For example, ask them to build a simple RAG prototype using a sample of your documentation. Ideally, you should involve your technical specialists to check test results and lead interviews.
But not every company has the internal resources for this kind of vetting. If you don’t have senior engineers available for technical interviews, you risk making hiring decisions based only on resumes or certifications.
That’s where DOIT Software can help. Our team provides an end-to-end technical evaluation and access to vetted AI talent across the USA, LATAM, and Europe. Simply share your requirements and receive the first relevant AI developer skills for your team within 5 business days.
The top AI developer skills for hiring in 2026 are Python plus a second language, data engineering and preprocessing, working with generative AI models like GPT, Claude, Gemini, and open-source LLMs, building RAG and vector-database systems, agentic AI with MCPs, and production deployment with evals and guardrails.
Soft skills matter on production teams too: clear communication, critical thinking, ethical judgment, and adaptability.
Hands-on experience with the major LLM APIs is the baseline: GPT, Claude, Gemini, plus open-source equivalents like Llama or DeepSeek. An AI developer should know the strengths and weaknesses of each family and pick the right one for a specific workflow.
Beyond model use, an AI developer should know three customization patterns. The first is RAG with hybrid retrieval and reranking. The second is tool use or function calling that lets a model interact with internal APIs and databases. The third is familiarity with MCPs, the 2026 open standard for plugging agents into external tools.
Yes, absolutely. While they can use low-code platforms (e.g., Microsoft Power Platform, n8n, Appian), a professional AI developer requires strong programming skills, often in Python. They use code to connect to model APIs, manage data, build core logic, and integrate the model into the application that ships.
The most in-demand AI developer skills in 2026 include proficiency with major generative AI models like GPT, Claude, Gemini, hands-on experience with application frameworks like LangChain, LangGraph, LlamaIndex, and CrewAI, expertise in retrieval-augmented generation, tool use through MCPs, and a working understanding of agentic AI orchestration.
