Discover how to hire skilled AI developers to meet your project needs effectively. Read on for practical tips and insights to find the right talent.
In 2025, AI is everywhere, and an artificial intelligence developer is an in-demand profession. From AI-powered chatbots to predictive analytics and generative models, artificial intelligence is transforming industries fast. McKinsey reports that 78% of organizations are now using AI models in at least one business function, and these numbers are growing. However, with the increasing demand for AI tools and experts, there’s a considerable challenge when hiring AI developers who bring real results.
This blog post is here to help. We'll walk you through what AI software engineers actually do, when it makes sense to hire one (and when it doesn’t), what to look for in talent, and where to hire AI developers.
AI is no longer a futuristic idea. It’s used in the daily workflows of companies in different industries and business segments, including SaaS and eCommerce, healthcare, finance, marketing, and logistics. etc.
43% of respondents say that the use of artificial intelligence in IT has led to cost reductions, which makes AI a strategic advantage for businesses.
According to Statista, the most common use cases for AI in development include automated code generation, searching for answers, documentation, bug detection, and content generation.
The considerable increase in AI use has driven an equally large demand for AI engineers. Companies want hire full stack developers who not only understand machine learning but can also integrate AI into products, improve internal tools, or automate repetitive dev work. Consider that not all “AI developers” are equally skilled.
Some candidates can talk about neural networks but haven’t actually deployed a model to production. Others might list ChatGPT on their resume without ever having fine-tuned or integrated an LLM into a real product. As a result, many business leaders end up hiring unskilled workers, wasting time and money on the wrong fit.
Hiring AI talent deals with understanding what skills your project really needs and finding people who can apply them in practice.
In practice, AI development covers a range of roles and specializations. Some focus on building the models themselves, others on understanding and preparing the data, and some on integrating AI into actual products. Let’s break it down so you can map each role to your business’s needs.
These are the people who build, train, and deploy models. Whether it’s predictive analytics, natural language processing (NLP), or computer vision, ML engineers work with data pipelines, experiment with algorithms, and tune models to get the best performance. They also handle model evaluation, testing, and scaling in production environments.
Hire an ML engineer if you need to build or deploy AI functionality like recommendations, image recognition, or text analysis from scratch.
Data scientists focus on making sense of large and often messy datasets. They explore, clean, and analyze data using data analysis and supervised learning algorithms, run experiments, and surface insights that allow a data-based decision-making process. In AI projects, they often work closely with ML engineers to define features, structure data, and evaluate outcomes.
Hire a data scientist if you have access to rich data management and want to extract patterns, trends, or predictive insights.
These are the people working at the cutting edge of AI. They explore new technologies, improve existing models, and experiment with things like generative AI and large language models (LLMs). Instead of just using existing tools, they try to invent better ones. Their work often includes testing new ideas, writing research papers, and finding ways to push the limits of what AI can do.
Hire a research engineer if you’re building a tech startup, creating a novel AI solution, or customizing foundation models in-house.
AI product engineers are the ones who make sure the AI in your product doesn’t just work in theory but also delivers scalable solutions. They take the smart stuff built by machine learning engineers and plug it into your actual app or platform. Their job is to make sure the AI features are fast, stable, and easy to use.
They care about how the product feels to the user. That means things like building the right APIs, fixing bugs when the AI models don't behave as expected, and making sure everything runs smoothly in real time.
Hire an AI product engineer if you’re launching an MVP or scaling your product and want AI features to work smoothly and effectively.
If you’re ready to turn AI-powered ideas into real, user-focused products, partner with our AI software development company.
Hiring an AI developer should be a strategic move — not a trendy experiment. Below are the situations where bringing in AI talent is the right call, and when you might want to hold off.
If AI is at the heart of your product — like an AI-powered SaaS tool, recommendation engine, or generative content platform — then an AI developer isn’t optional, it’s essential. These products require custom models, ongoing experimentation, and a deep integration between AI and your core infrastructure.
AI can drive serious value — but only when there’s a defined problem to solve. Scenarios like:
...are all use cases where custom AI logic, including complex algorithms, may outperform off-the-shelf tools. If your pain point is measurable and high-impact, that’s your green light.
Before you bring in technical talent, make sure the use case is backed by data. Ask yourself:
AI developers can do a lot — but they can’t compensate for missing or poor-quality data. Validation first, engineering second.
Tools like OpenAI’s APIs, Google Vertex AI, or AWS solutions are great starting points. But if:
…then it's time to move beyond drag-and-drop tools and build something tailored to your product and users.
If your plan is simply to “do something with AI,” take a step back. Hiring AI engineers without a clear vision or problem to solve is a fast track to wasted time and budget.
Get clarity first. Know the problem. Understand how AI can help. Then — and only then — build the right team to make it happen.
If you want to hire AI engineers who deliver real results, you need to apply a profound approach to the search. It definitely goes far beyond checking their technical skills. Even though tools and frameworks are very important, you also want someone who can think critically, handle ambiguity, and find non-obvious solutions. Here's what to prioritize:
Python remains the dominant language in AI development, and your ideal candidate should be fluent in it. The expert needs to be not just syntax-wise, but also skilled in using it to build efficient data pipelines, train models, and integrate AI into applications. A strong grasp of machine learning fundamentals (supervised/unsupervised learning, model evaluation, overfitting, etc.) and deep learning concepts (neural networks, CNNs, RNNs) is non-negotiable for more advanced roles.
Frameworks like TensorFlow, PyTorch, Keras, or Hugging Face aren’t just nice to have; they’re critical to have for an AI engineer. An experienced specialist should know when to use which, understand the tradeoffs, and be able to build and fine-tune models with them. Bonus points if they’re comfortable with related tools like OpenCV, Scikit-learn, or LangChain for AI applications.
With the increasing amount of data nowadays, it’s not enough to be aware of basic principles of web design vs web development. The profound skills of data engineering methods and working with datasets are a must for the AI engineer. Look for candidates who’ve dealt with real-world data challenges, not just polished academic datasets. Their ability to engineer features, handle missing data, and operate with datasets often makes or breaks a successful AI project.
A working prototype in a Jupyter notebook is just step one. Can this professional for outsource web development take an AI model and put it into production? They need to think about performance, handle challenging cases, update models, and integrate into broader systems.
There’s no doubt that each business targets revenue growth, not just hyping trends. That is why great AI engineers need to think critically in order to find the best solution. They ask smart questions, challenge assumptions, and work collaboratively to frame the problem correctly. They understand the business context, whether it’s increasing conversion rates, automating workflows, or applying data science, including statistical methods and natural language processing.
AI projects rarely follow a linear path. You’ll want someone who thrives in environments where iteration, experimentation, and occasional failure are part of the process. Look for people who can communicate clearly, prioritize work, and stay flexible in the face of shifting data, tools, or goals.
In our recent blog posts we’ve already described how to hire a graphic designer and where to hire mobile app developers. Now let’s discuss where to hire AI engineers. Whether you need a fast prototype, a long-term product evolution, or a specialized AI feature embedded in your app, there are several ways to hire the right talent that enhances human intelligence.
In case you're building a quick MVP, testing an idea, or just need short-term support, looking for software outsourcing on freelance platforms can be a great start. Marketplaces like Toptal (which pre-vets its talent) or Upwork (more open and flexible) can help you hire remote developers with different backgrounds and rates.
Pros and cons of hiring freelance AI engineers:
✅ Fast to hire — you can often start within days
✅ Flexible — easy to scale work up or down
✅ Cost-effective for small tasks or early-stage needs
❌ Quality varies, especially on open platforms like Upwork
❌ Less commitment and ownership from software engineers
❌ May lack collaboration and long-term thinking
Read more: The best Upwork alternatives.
If you’re looking for a reliable partner to design, build, and scale AI-powered products, consider a full-cycle development company that offers a no-risk trial period. In this case, consider Empat an award-winning software development agency with deep AI expertise.
Providing qualitative backend software engineer services, Empat works with startups and growing companies to turn business goals into working AI-powered products with multiuser capabilities. From building custom ML algorithms to integrating models into scalable backend systems or intuitive interfaces, our team combines product strategy, engineering, and design in one workflow.
Pros and cons of hiring AI development agencies
✅ Full-cycle development from idea to launch
✅ Close collaboration and structured project delivery
✅ Product-minded engineers who think beyond code
❌ Not as cheap as hiring solo freelancers
❌ May require more onboarding if your goals are vague
These platforms specialize in AI and machine learning talent, offering a curated pool of AI specialists with relevant experience. Turing uses deep vetting and global sourcing, while vTeams builds long-term remote teams.
Pros and cons of looking for AI-skilled developers at marketplaces
✅ Specialized expertise in AI/ML
✅ Quality assurance through platform screening
✅ Can scale up a team fast
❌ May require longer contracts
❌ Some platforms have rigid hiring processes
❌ Not always a cultural fit with your in-house team
If you're building a core AI product or embedding AI deeply into your company’s operations, hiring full-time developers makes sense. Platforms like LinkedIn, Wellfound, or AI-specific job boards help you find candidates looking for permanent roles.
Pros and cons of hiring an in-house AI software engineers
✅ Full-time commitment and cultural alignment
✅ Long-term ownership and deep product understanding
✅ Easier to build internal expertise
❌ Slower to hire (can take months)
❌ Higher upfront investment (salaries, onboarding)
❌ Requires strong internal management
Each of these options has its place, depending on your business development stage, the relevance of emerging technologies, budget, risk tolerance, and whether you’re solving a tactical task or building a core capability. At the same time, your best fit can also become a hybrid approach. You may start with an agency or find a web developer on the freelance platform, and then transition to your own in-house team, which will allow you to balance flexibility and long-term impact.
At Empat, we use a comprehensive approach to AI development. We build skilled teams of artificial intelligence developers that think strategically, work with sophisticated algorithms, adapt fast, and integrate seamlessly with your business goals. Whether you're looking to launch a feature powered by machine learning or scale an AI-native product, our approach ensures you get the right people, not just available ones.
We’ve built a culture where developers are more than coders — they’re product contributors with deep technical foundations and strong communication skills.
Empat works with founders and product leaders to scope, build, and launch AI-powered solutions, guiding teams from early idea to working product. Unlike traditional staffing firms or freelancer platforms, Empat builds cross-functional teams — including not only AI/ML engineers, but also product managers, designers, and developers — to make sure what you build isn’t just technically sound, but user-friendly and market-ready.
We’ve helped hire for startups and large companies across fintech, healthtech, education, and SaaS launch AI-driven features and platforms that work at scale. Our AI engineers are skilled in mobile app design and development, fintech app development, AI app development, etc.
One example is BigSister AI — a smart system we built to help sales departments monitor performance, predict outcomes, and optimize team productivity using real-time data and machine learning.
This project required seamless integration with multiple CRMs and data sources — a challenge we solved by combining AI expertise with solid product engineering. It’s just one of many examples where our AI teams not only shipped innovative features but also solved complex technical problems under real-world constraints.
With Empat, you can get started in as little as 7–14 days, with onboarding and kick-off led by product-savvy team leads. We ensure each developer integrates quickly, understands the business logic behind your project, and can begin contributing right away.
The most successful AI projects don’t begin with algorithms — they start with a clear understanding of the problem. Before you dive into hiring, take time to define what you’re trying to solve, how AI fits into that vision, and what kind of expertise, including machine learning techniques, will truly move the needle.
Not every project needs a deep learning expert. Sometimes you need a generalist who can work across systems, or a product-minded engineer who can ship fast and iterate. That’s why scoping comes before staffing — so you know whether to look for a machine learning engineer, a data scientist, or a full-stack AI team.
Above all, hire for problem-solving ability, not just credentials. The best AI developers don’t just build models — they ask the right questions, translate business goals into logic, and build things that work in the real world.
And when you choose who to work with, look for partners who understand products, not just code. Because in AI, a working feature that solves a real problem will always outperform a perfect model with no users.
Build smarter with the right AI team. Talk to Empat today and tap into our vast knowledge to turn your AI idea into a working product — fast, focused, and built to scale. Let’s Get Started
The cost of hiring an artificial intelligence engineer varies widely based on experience, location, and project complexity. Junior developers may start around $40–60k per year, while senior specialists in fields like computer vision or consultants can command $100k+ annually or higher for contract work. Outsourcing software development or working with agencies can offer more flexible pricing based on your project needs. Reach out with your project details, and we’ll provide a personalized cost estimate tailored to your needs.
Start by clearly defining the problem you want to solve and the AI skills required. Decide whether you need specialists like machine learning engineers, data scientists, or generalist product-focused developers. Look beyond credentials—prioritize problem-solving ability, communication skills, a basic understanding of handling raw data, and delivering machine learning solutions. Consider partnering with teams like Empat that provide cross-functional AI experts aligned with your business goals.
The cost of AI development depends on the scope, team size, and timelines. Small projects or proof-of-concept builds might range from $20k to $100k, while full-scale AI product development can exceed several hundred thousand dollars. Factors like machine learning development, data preparation, model training, integration, and ongoing maintenance also affect the budget. If you want us to calculate the cost of your AI project, drop us a message with your business description and tasks — we’ll get back to you with a detailed pricing estimate.
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