Learn how to choose between an AI engineer and an ML engineer by understanding who delivers real product value, cuts waste, and ships working systems faster.

Hiring strong AI engineers has never been harder. Every candidate claims hands-on expertise, yet few can prove they’ve shipped scalable AI systems that actually work in production.

And that's not the only reason that explains the difficulty of hiring a good AI engineer. According to Aura Blog, job postings for AI-related roles more than doubled between January and April 2025 (jumping from 66,000 to nearly 139,000). That growth explains why your hiring process feels more competitive and far less predictable.

This guide breaks down exactly how to evaluate real AI engineering capability.

Learn when to hire an AI engineer vs. a software engineer by comparing ROI, project needs, and team readiness to make smarter, data-driven hiring decisions.

New York’s demand for senior AI talent keeps rising while local hiring cycles slow down under salary bands that usually pass $150,000 for mid-level roles. But there is a better path, and this guide walks you through it step by step. 

Here, you'll compare your options, assess risks, and gain a clear hiring path.

First, let’s break down what AI engineers actually do so you can pick the right service.

As we move through 2025 and into 2026, the role of the AI Engineer in your company has moved from a bonus skillset to a business necessity. The days of running a few isolated machine learning models are fading. Now, businesses are embedding artificial intelligence across everything, from product innovation to operations and real-time decision-making.

And the shift is showing up in the data. 

The PwC 2025 Global AI Jobs Barometer found that nearly every industry is increasing its use of AI-exposed roles. It found that 66% of jobs most impacted by AI are experiencing faster skill evolution than those that aren’t. 

At the same time, job postings for AI roles nearly doubled, jumping from around 66,000 to almost 139,000 between January and April 2025.

AI and machine learning engineers are the minds behind the smart tech we use every day. Whether it’s powering breakthroughs in healthcare or streamlining financial systems, they build the tools that learn, adapt, and perform better over time. That way, businesses and people work smarter.

Besides, recent data by Pave shows that 83 % of job titles in their AI/ML dataset include ‘Machine Learning’ or ‘ML’, emphasizing how central ML-engineer roles have become in the field.