Need to Hire An AI Engineer? Here’s What You Need to Know

1. Clearly defining project scope

Before you dive into hiring, you’ll want to define the project scope. What are you asking the AI engineer to accomplish? Are you integrating AI into an existing product, or are you looking to build something entirely new from scratch? The scope of your project will directly impact the skills and experience you need in an AI engineer.

Clarifying these goals upfront will prevent confusion and costly missteps later on, giving your new hires a clear understanding of their responsibilities from day one.

2. Choosing remote or on-site hiring

Deciding whether your AI engineer should work remotely or in the office significantly affects your talent pool and daily operations.

Remote hiring offers substantial advantages, such as access to a global pool of specialized candidates and lower overall hiring costs. 

However, it also comes with challenges, including communication barriers, potential time zone differences, and collaboration complexities, depending on where you hire. Many businesses prefer hiring remotely once they’ve established clear processes for managing distributed teams.

In contrast, on-site engineers simplify collaboration and improve real-time problem-solving. Yet, limiting your search geographically often means increased competition and higher salaries. 

If you’re leaning toward remote work, having a structured plan for hiring a remote AI engineer means a smoother integration into your existing workflow.

3. Budgeting and cost expectations

Budget is an important consideration because AI engineer salaries vary greatly depending on location. For example, in the US, talented generative AI engineers regularly command six-figure salaries due to high competition.

In our experience, here’s how much you might save by choosing an offshore region such as Latin America:

The significant difference between salaries stems from local living costs and regional talent availability. Many businesses strategically choose to hire offshore or nearshore AI engineers to access highly skilled professionals at a lower cost, stretching their budgets further. 

Understanding these cost dynamics helps you allocate your resources effectively and realistically plan your hiring budget.

4. Choosing the right hiring method

Finally, you’ll need to select your hiring method. You could choose the DIY approach, using job boards or freelance platforms, but this requires significant time and effort to screen applicants effectively. 

An alternative is to partner with specialized AI staffing companies that streamline the hiring process for you. Staffing agencies have deep experience sourcing candidates with specialized skills, quickly matching your company with qualified AI talent. 

Given how competitive the market is, partnering with recruitment specialists often leads to faster hiring timelines and better candidate quality, ultimately allowing your business to move forward confidently in its AI initiatives.

Step 1: Define the AI engineer role clearly

Start by clearly defining the role and responsibilities of your future AI engineer. Vague descriptions often attract the wrong candidates, costing valuable time. Be specific about the technologies they’ll work with, expected outcomes, and the types of problems they’ll solve. 

For example, instead of a generic job ad, clearly state something like: “Develop and implement predictive machine learning models to forecast customer behavior in our e-commerce app.” To simplify this process, you can use our free job description generator.

Step 2: Decide on your hiring location and model

At this point, you need to make a clear decision on your hiring approach.

Will you build an in-office US team or tap into global talent pools?

The US option gives you face-to-face collaboration but comes with the talent shortage and cost challenges we’ve discussed. The offshore route—particularly with Latin America’s time zone alignment—lets you access specialized skills while significantly reducing costs.

Also determine whether you’ll handle recruitment directly or work with a specialized partner. Having your hiring approach settled early streamlines everything that follows, from budgeting to interview logistics.

Step 3: Source and screen candidates

If you’re handling hiring in-house, screening is critical. Focus on resumes that show hands-on work with machine learning frameworks like TensorFlow or PyTorch, plus experience in data modeling and real-world AI deployment.

Certifications and academic projects are helpful, but actual shipped products and performance metrics are more valuable.

Make sure candidates can demonstrate a working knowledge of AI architecture and show clear contributions to past AI projects.

If you’re partnering with a recruiter or staffing agency, the good news is they’ll do most of this legwork for you. A specialized partner will already know what to look for and can bring you a shortlist of vetted candidates who match your technical and cultural requirements.

Step 4: Conduct effective interviews

Interviewing an AI engineer goes beyond basic technical questions. While it’s essential to test their understanding of machine learning concepts, neural networks, and programming proficiency, you also want to evaluate their ability to communicate complex technical concepts clearly. 

For example, you could ask: “How would you explain machine learning to someone with no technical background?” or “Can you walk me through a time when you had to simplify an AI-driven solution for stakeholders?” 

This makes sure that you’re not just hiring a technically capable engineer but also someone who can collaborate effectively across your business.

Step 5: Negotiate and finalize the hire

Once you’ve found your AI engineer, don’t fumble at the finish line. The way you handle this final stage sets the tone for your entire working relationship.

If you’re hiring in the US, be prepared for the competitive reality of AI talent and make an offer that will be attractive.

For international hires, do your homework on local market rates. What’s competitive varies dramatically by region, and understanding these differences helps you make an attractive offer without overpaying.

This is where working with a recruitment partner pays off—they’ll have current insights on what constitutes a competitive package in specific markets, including both salary expectations and standard benefits like vacation time, bonuses, or flexible schedules.

Make your expectations crystal clear from day one. Outline specific projects they’ll tackle first, who they’ll be working with, and how success will be measured. The best AI engineers want to know their work matters, not just that they’re filling a seat.

Finally, have a solid onboarding plan ready before they start. Schedule regular check-ins for the first few weeks, assign them a go-to person for questions, and make sure they have access to all the tools and documentation they’ll need. This upfront investment pays off in faster productivity and better retention.

What does an AI engineer do, and how is that different from a software engineer?

An AI engineer builds machine learning models and intelligent systems that learn from data, while a software engineer typically focuses on infrastructure or general-purpose app development. 

Is an AI software engineer the same thing as an AI engineer?

They’re closely related, but not identical. An AI software engineer focuses on integrating AI into software systems, while an AI engineer may design and train the models themselves.

What is an AI DevOps engineer?

An AI DevOps engineer manages the deployment, testing, and monitoring of machine learning models. They bridge the gap between data science and operations, making sure AI systems run smoothly in production.

What is an AI backend engineer?

An AI backend engineer builds and maintains the server-side infrastructure needed to run AI models efficiently in real-world applications. They often focus on APIs, databases, and system architecture to support AI functionality at scale.

What is an AI security engineer?

An AI security engineer focuses on protecting AI systems from threats like cybersecurity and data breaches, model manipulation, and adversarial attacks. They design safeguards to keep both the models and the data they rely on secure.

Where’s the best place to hire nearshore or offshore AI engineers?

Top destinations include Mexico, Brazil, Colombia, and Argentina due to cost, time zone alignment, and strong technical education.

How much does it cost to hire an AI engineer from Latin America?

Salary expectations for AI engineers in LatAm are typically 30–70% less than in the US, with salaries ranging from $42k to $96k.