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Most companies making their first AI hire don't yet know whether they need a machine learning engineer, a generative AI engineer, or a data scientist. They just know they need someone who can ship AI-powered features. Figuring that out alone costs time: the median technical role in the US takes 62 days to fill, and that's before accounting for the extra weeks spent deciding which title to even search for. Hire an AI engineer through KDCI instead, and you get a pre-vetted generalist, matched in 7-14 days, at a flat monthly rate roughly a third below a comparable US hire, plus a clear path to a narrower specialist if that turns out to be what you actually need.
An AI engineer builds and deploys AI-powered features end to end: integrating models, whether built in-house or accessed via API, building the surrounding application logic, and shipping the result to production. The role sits closer to software engineering than to research. A typical job description covers things like wiring a model into an existing product, handling the edge cases a model gets wrong, and making sure the feature actually holds up once real users touch it, not just in a demo. It's the generalist entry point into AI hiring, and the right first hire when your scope is still broad rather than narrowly defined.
Worth stating plainly: "AI developer" and "AI engineer" are used interchangeably across job postings and search behavior. There's no clean, universally agreed distinction between the two titles in practice, so don't over-index on which word a job post uses. If you're comparing this hire against the full landscape of AI roles, our guide to AI developer hiring covers the broader picture this page sits inside.
You may also see this generalist work described as "applied AI engineering," a title more common at AI-native companies. It describes the same core job: building on top of existing models rather than training them from scratch.
"AI engineer" is a starting point, not a final answer. Depending on what you actually need, one of these more specific roles might fit better:
As a team's AI program matures further, some eventually need even more specialized seats: an AI Agent Developer, a Forward Deployed Engineer, or a senior AI Solutions Architect.
The average AI engineer salary in the US is $106,386 a year, with the middle 50% of postings ranging from $76,000 to $132,500, and the full range spanning $44,000 to $173,000. Demand is real: AI engineering hiring runs at roughly 1,550 new US roles a week, with 43,480 postings tracked since January 2026. Most of that demand concentrates at the experienced end of the market, which is exactly why a generalist search takes real effort rather than a quick job-board post.
Time to fill compounds the problem. The median US technical role takes 62 days to fill. KDCI places pre-vetted AI engineers in 7-14 days instead, at a flat monthly rate roughly a third less than a comparable US hire.
By the Numbers
Screen for production coding ability first, not just familiarity with AI tools. Beyond that, look for real model-integration experience, whether through APIs or self-hosted models, applied ML fundamentals sufficient to know when a model is the right tool, data-pipeline literacy since features rarely run on clean data alone, and deployment and observability awareness so a feature doesn't just work in a demo.
On certifications: a certification is one weak signal among several, useful context but not a substitute for a real skills assessment. Plenty of certified candidates have never shipped anything to production, and plenty of strong engineers have no certification at all. Ask instead about a specific feature they built, what broke once it hit real usage, and how they fixed it. That's exactly the gap a proper vetting process closes.
The role is only half the decision. How you hire matters just as much.
If you've already decided offshore is the right model and want a deeper look at that specific channel, our guide to hiring an offshore AI engineer covers it in more detail.
Every candidate is pre-vetted via an internal skills assessment confirming deployment readiness, applied here specifically to production coding ability and real model-integration experience, not just familiarity with AI tools.
You share the scope, KDCI matches you with a shortlist of pre-vetted candidates, you interview on your own criteria, and your pick starts within 7-14 days.
This page's real value isn't just speed, it's helping you confirm this is the right title before you commit to it, rather than the fastest way to fill whatever title you happened to search. Get that wrong and you end up with a mis-hire that costs far more than the search itself. Once the title is settled: pre-vetted talent, matched in 7-14 days, at a flat monthly rate roughly a third less than a comparable US hire. Whether you need one generalist or broader AI development services, the same pre-vetted, remote-first approach applies, and our AI team structure guide maps this role alongside the rest of the ten-role framework if you're scoping more than one hire.
Meet Your Vetted AI Engineers Tell us what you need built, and we'll match you with pre-vetted AI engineers ready to start in 7-14 days. Speak with an outsourcing specialist to get started.
There isn't a clean, universally agreed distinction. The two titles are used interchangeably across job postings and search behavior, so focus on the actual role description rather than which word is used.
An AI engineer ships AI-powered features end to end, often integrating existing models. A machine learning engineer trains and tunes models from your own data. See the comparison table above for the full breakdown across every role in this cluster.
7–14 days, pre-vetted, against a US median technical-role benchmark of 62 days.
No. A certification is one weak signal among several, useful context but not a substitute for a real skills assessment focused on production experience.
Roughly a third less than a comparable US hire, at a flat monthly rate.
