Hire AI developers: Salary benchmarks, team structures, and vetting process

A few years ago, AI was a rare technology used by only a few teams across fields. Machine learning adoption was celebrated but not required. In 2026, this is no longer the case. An AI engineer role ranks first on LinkedIn’s Jobs on the Rise this year. Most platforms see AI as part of their core feature set, and users expect some kind of machine learning assistance across most industries.

With generative AI, agentic AI, and other machine learning advancements, not leveraging deep learning and related technologies would make most companies outliers in an increasingly AI-enhanced world.

Key points of the article

• Specifics of the AI engineering job function
• Salary benchmarks for in-house teams and freelancers
• AI team structure
• Different approaches to recruiting an AI developer
• Hiring process for AI developers at Xenoss

Why do teams hire AI developers?

Seeing how artificial intelligence helped offset recession fears, business leaders and investors felt a sense of urgency. Indeed, machine learning can add trillions of dollars in value to most industries, but tapping into the market requires a specialized team.

While experienced software architects can transition into AI engineering to cover your organization’s machine learning needs, having an expert on board with an excellent command of specific AI tools and technologies increases the odds of product success.

Here are the AI engineer responsibilities that drive progress in product teams:

• Guide product design to ensure that AI helps achieve business goals and delivers value to end users.
• Manage research and development efforts to determine which AI tools and technologies would deliver the highest ROI.
• Offer the most accurate and cost-effective solutions to a specific problem.
• Navigate the regulatory landscape, monitor potential challenges in deploying AI models, and design workarounds.
• Explain AI/ML technologies to non-technical teams and help them leverage machine learning.

Is it difficult to hire an artificial intelligence engineer?

In the last two years, tech companies have become increasingly aware of the importance of leveraging AI. As a result, demand for AI talent has grown exponentially, while supply has failed to keep pace. To understand the scale of the talent shortage, we examined data from global sources.

• A WEF report highlights that large segments of the global workforce will need reskilling to meet rising AI demand, a dynamic that continues to make skilled AI engineers and related roles among the hardest to hire for.
• The top ten fastest-growing Information and Communication Technology (ICT) jobs are: 
  • AI Risk & Governance specialist (234% of job demand growth); 
  • NLP Engineer ( 186%)
  • AI/ML Engineer (145%) 
  • AI Business Consultant (134%)
  • AI Infrastructure Engineer (124%) 
  • AI/ML Researcher (98%)
  • Cloud Engineer (89%) 
  • Cyber Threat Intelligence Consultant (84%)
  • Data Scientist (76%) 
  • Automation Engineer (72%)
• Cisco’s Chief People Officer, Kelly Jones, admits that filling operational AI and ML roles is difficult. She says, “The qualified pool is so small, and the demand is so high”. Senior executives across large companies like OpenAI, Meta, and Cisco have to personally get on the call with the best candidates to secure them.
• 50% of executives consider a talent shortage a key barrier to scaling AI initiatives in the engineering, research, and development (ER&D) domain, and 58% say that there isn’t enough engineering talent with the necessary AI skills.

This data shows that hiring AI engineers is a global challenge for businesses, regardless of their size.

In startup hubs, such as Silicon Valley, Boston, NYC in the US, or London, Paris, and Berlin in Europe, finding a skilled and affordable engineer is a struggle due to the many high-profile offers and high AI developer salaries.

Salary benchmarks across countries and regions

Salary benchmarks for AI and ML engineers vary significantly by country, seniority, and specialization. The figures below reflect median base salaries and do not include additional employment costs such as software tooling, hardware, payroll taxes, medical insurance, equity, bonuses, or compliance overhead, all of which increase the fully loaded cost of an in-house AI team.

Findings are from StackOverflow and Glassdoor.

Key takeaway: US-based AI engineers command the highest compensation globally. In practice, compensation frequently exceeds median values when companies require senior-level engineers, deep ML expertise, or experience with production-grade AI systems.

AI engineer compensation by seniority (United States)

Source: 2026 US Market Report by MRJ Recruitment

These ranges reflect base salary only. Once benefits, payroll taxes, tooling, security requirements, and ongoing training are included, the total annual cost of a senior or staff-level AI engineer in the US is often 30–50% higher than base compensation.

Europe: lower salaries, higher regulatory readiness

The European AI engineering market is generally more cost-efficient than the US, with typical salaries ranging from €60,000 to €100,000, depending on the country and seniority.

A key differentiator is regulatory familiarity. European AI engineers are increasingly required to work within the constraints of the EU AI Act, currently the most comprehensive AI regulation globally. As a result, many European teams have hands-on experience with:

• Risk classification of AI systems
• Data governance and model transparency requirements
• Compliance-by-design approaches to AI development

For organizations operating in or targeting the European market, this regulatory expertise can reduce legal risk, rework, and time to approval, an important factor beyond pure salary comparison.

Hourly rates: Freelance AI engineers

For companies seeking maximum cost flexibility, hiring AI engineers on an hourly basis is often the most affordable entry point.

Source: Upwork.

However, while freelancers can reduce short-term costs, AI initiatives carry higher-than-average delivery risk due to:

• Fragmented ownership of data, models, and infrastructure
• Limited accountability for production reliability and security
• Lack of formal guarantees around quality, continuity, and compliance

Choosing the right engagement model

For organizations building business-critical or regulated AI systems, partnering with an enterprise AI engineering company such as Xenoss offers a middle ground between in-house hiring and freelancing.

You gain:

• Access to senior AI developers for hire at freelance-like rates
• A structured delivery model with formal SLAs
• Clear accountability for quality, security, and long-term maintainability

This approach reduces execution risk while avoiding the fixed overhead and hiring delays associated with building a full internal AI team from scratch.

AI Engineering team structure

A lack of AI engineering expertise leaves 88% of AI projects at the proof-of-concept stage. Building a balanced team is vital to avoid stagnation and push the project ahead.

Xenoss has over 15 years of experience in building high-performing AI teams. A consistent finding that emerged over time was that no two teams were alike in the roles they prioritized. Depending on the scale of the project (internal tool, narrowly specialized user-facing tool, or multi-purpose large-scale platform), the list of people who should steer the project varies, and the emphasis on ethics and regulations can sometimes be more pronounced.

Every step of data collection, processing, and deployment as part of an ML model aligns with a specific role:

Data engineer responsibilities

• Build and test ETL pipelines
• Architect SQL and NoSQL data stores
• Build strategies for data processing, integration, transformation, and storage
• Oversee AWS/Google Cloud/Microsoft Azure maintenance
• Collect, clean, and filter structured and unstructured data

Data scientist responsibilities

• Align with business stakeholders on high-priority problems
• Collaborate with data engineers
• Test machine learning models
• Support other teams (sales, marketing, product) with data needed for strategic decision-making

Data analyst responsibilities

• Apply large data sets to solving business problems through a range of analytical and statistical tools
• Help identify success metrics in product teams, build growth projections, and monitor the progress across selected metrics
• Use data to identify emerging trends and opportunities that help steer the product
• Closely partner with engineering, product, marketing, and other teams to inform their reasoning

AI developer (ML engineer) responsibilities:

• Deploy, maintain, and scale machine learning models
• Engineer the infrastructure surrounding machine learning models
• Platform engineering and MLOps: develop and administer Kubernetes clusters
• Security scanning and investigations
• Release engineering

These are the roles directly involved in building AI models. Other professionals typically support these functions:

• Project manager responsible for overseeing the project lifecycle: defining project scope, goals, timeline, budget, etc.
• Domain expert: a professional who provides domain expertise and context for machine learning models. In some cases, this role can be carried out by AI engineers themselves if they are well-versed in the project’s field.
• Systems Architect helps build a suite of machine learning tools within the organization’s IT framework, ensuring alignment between ML initiatives and broader organizational goals.
• AI data analyst specializes in using artificial intelligence tools and techniques to analyze complex datasets. This role requires a deep understanding of machine learning, data mining, and statistical analysis to extract meaningful insights and inform business strategies.
• AI architect: responsible for building an enterprise-wide AI pipeline for the organization. These professionals also play a role in connecting other members of the engineering team: data scientists, DevOps, MLOps, and business leaders.
• AI product manager: oversees the development and implementation of AI-based products, balancing technical feasibility with market needs and user experience. This role involves strategic planning, cross-functional collaboration, and a deep understanding of AI technologies to guide the product lifecycle from conception to launch.

We’d like to point out that a cookie-cutter approach is typically ineffective when assembling an AI engineering team. Instead, it’s better to look for tech professionals with specialized skill sets that align with AI technologies and the tools the product team has in mind.

Here’s an example of how the critical skills of AI engineers on a team can vary depending on the type of final product.

Hire AI developer: Job description examples from OpenAI and other companies

After defining which AI engineering roles can enable fast, efficient AI software development, team leaders should focus on finding professionals whose skills align with their responsibilities.

Rather than relying on a one-size-fits-all approach, we recommend crafting a custom job opening tailored to your domain, product or service type, budget, and expected responsibilities for each AI role.

However, having a clear understanding of what top companies are listing in AI developer openings can help align expectations with the reality of current AI development tools and technologies.

To help engineering team leaders create job descriptions that attract skilled talent, we analyzed how top AI players craft job descriptions for a range of roles.

Hire AI engineers: Three widely used approaches

The tight AI engineering job market calls for open-mindedness and creativity in hiring decisions. Hiring a full-time in-house engineering team has been the industry standard for a long time, but difficulties in securing talent and a fluctuating economy are challenging that practice.

Alternative approaches to hiring, like relying on contractors or committing to outstaffing, are gradually becoming more widespread among organizations.

Let’s examine their strengths and shortcomings to draw a line between these ML developer hiring strategies.

There are different ways to use outstaffing to hire AI engineers. For example, tech teams can use the model for point-based hiring (e.g., hire AI engineer to strengthen existing teams) or for building entire AI teams from scratch.

Look at the projects where Xenoss recruiters helped source AI engineers and related specialists: data scientists, analysts, and other professionals.

How we work at Xenoss

Xenoss has supported teams in machine learning, data engineering, and AI adoption for over 15 years. When beginning a new project, we focus on building a team with a deep understanding of the client’s domain (including AdTech, MarTech, manufacturing, healthcare, and financial services) and a robust set of machine learning tools and technologies. Through a series of technical interviews and culture fit assessments, we ensure that Xenoss AI engineers are a tight fit for the client’s project. 

Check out our detailed guide on how to work with AI and data engineering partners to find out how to map your business and technical requirements to the right AI and data expertise.

Xenoss has a robust pool of vetted and battle-tested AI engineers. If one of our developers meets the project’s requirements, we introduce them to the core team and schedule a technical interview. This approach allows us to cut hiring time and recruit skilled AI engineers in a matter of days.

If no AI engineers in our talent pool meet the client’s need, Xenoss hiring experts will source skilled candidates by sharing curated job openings in trusted tech communities.

Building a winning AI engineering team with Xenoss typically looks as follows:

Discovery call

Our engineering team assesses your project proposal to determine the type of AI expertise required. A deep assessment of the product plan and roadmap enables Xenoss recruiting experts to hire skilled engineers and deliver the solution with minimal time-to-market.

CV screening and preliminary assessment

Based on the client’s requirements, our specialists create detailed job descriptions that provide developers with a clear understanding of their responsibilities and required skills.

The candidates for each application are screened to match the following criteria:

• Proven track record in the relevant field
• Proficiency in using machine learning tools and frameworks (PyTorch, Scikit, NumPy, TensorFlow, etc.)
• Domain knowledge in the client’s industry
• English fluency
• Additional project-specific criteria

Vetting of shortlisted candidates

All candidates deemed skilled enough to move to the interview stage are thoroughly vetted by our HR department to ensure their experience, education profiles, and other data are legitimate.

Here are the steps of our vetting process:

• Contact the companies candidates worked at previously
• Confirm education and other credentials
• Validate the recommendations provided by the applicant
• Check publicly available social media profiles and other data sources

Interviews: Procedures and questions to ask

To confirm that an AI engineering candidate is a tight fit for the project, Xenoss’s recruiting team has developed a time-tested approach to interviewing applicants. We use a three-step process to gauge a candidate’s knowledge:

Step 1. Culture-fit interview

The HR department conducts a culture-fit interview to align expectations and determine whether the candidate aligns with the company’s culture.

Question examples:

• What type of work environment helps you perform at your best, and what tends to slow you down?

• Tell us about a situation where project priorities changed mid-delivery. How did you adapt?

• How do you handle feedback from non-technical stakeholders or clients?

• What motivates you most when working on long-term, complex projects?

• How do you typically collaborate with distributed or cross-functional teams?

Step 2. Deep technical interview

Our AI Engineering Lead prepares questions that assess the candidate’s prior experience and ability to apply skills from prior projects (e.g., deploying and scaling machine learning models, managing data pipelines, and infrastructure engineering) in the context of a client’s organization.

Question examples:

• Walk us through an AI or ML system you’ve taken from development to production. What challenges did you encounter after deployment?

• How do you approach model monitoring and performance degradation in production?

• Describe your experience building or maintaining data pipelines that support machine learning workloads.

• How do you decide between different model architectures or tools when working under business constraints such as cost, latency, or explainability?

• Tell us about a time when a model performed well in testing but failed in production. How did you diagnose and resolve the issue?

Step 3. Final interview

The HR department closes this cycle by discussing in more detail salary expectations, responsibilities, and collaboration models.

Question examples:

• What level of ownership do you expect to have over technical decisions in a client project?

• How do you prefer to communicate progress, risks, and trade-offs to stakeholders?

• What type of projects or AI use cases are you most interested in working on, and which ones would you prefer to avoid?

• How do you balance individual contribution with team-level accountability in delivery-focused work?

• What are your compensation expectations, and how do you evaluate offers beyond salary alone?

Based on a client’s preferences, our recruiters and the HR department, in collaboration with the client’s in-house engineering/executive team, develop test tasks to assess the candidate’s motivation and engineering skills. We focus on tailoring the assignment to the candidate’s day-to-day tasks and responsibilities.

Onboarding and continuous support

After assembling the AI engineering team that matches the client’s needs, Xenoss experts stay on standby and help the core team manage international talent by offering assistance in:

• Payroll and taxation
• Health insurance
• Legal documentation
• Benefits distribution

The ability to delegate administrative burden to Xenoss experts allows tech teams to refocus efforts from administrative minutiae to team management and collaboration.

Final thoughts

The AI engineering market is booming; over the next 7 years, it’s expected to grow at a 30.6% compound annual rate.

Interest in machine-learning-enabled projects among users and investors is high, encouraging product teams to explore and adopt these technologies.

A growing talent shortage of skilled developers is the side effect of the AI boom. To stay afloat in a highly competitive talent market, tech leaders need to think beyond the standard hiring playbook and embrace alternative hiring practices, such as outstaffing.

At Xenoss, we helped startups leverage the power of outstaffing to successfully integrate AI in software development. Explore our work to see the impressive performance and cost-reduction results our AI engineers helped diverse organizations achieve. To discover how outstaffing can support your AI development project, get in touch with our team.