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If your engineering team spends more of the week firefighting deployments than shipping features, the codebase is rarely the cause. Pipelines fail without warning, cloud environments get configured by hand, and once something ships, nobody clearly owns keeping it running.
That gap widens as the stack grows, and it gets more expensive every sprint it stays open. A dedicated DevOps engineer closes it, and KDCI can place one on your team in 7–14 days. Below is what the role covers, where it stops and adjacent titles begin, and which hiring channel fits your timeline and budget.
A DevOps engineer owns the systems that move code from a repository into production and keep it running there. In practice, the work covers five areas:
The same discipline that keeps a web app deploying cleanly keeps model deployment pipelines and GPU infrastructure running under load. Workflow and business-process automation sits outside this role, along with QA and test automation, which is the work you would hire Automation Engineers for.
If you are still mapping the seats on your team, KDCI's guide to AI team structure shows where DevOps sits alongside the modeling and data roles around it.
DevOps owns the path to production, Automation Engineers own business workflows and test coverage, SRE owns measured reliability, and MLOps applies DevOps practice to model pipelines. Here is how they compare on the details that decide which one you post:
SRE is a distinct discipline built around formal reliability targets, and it usually exists as its own function inside larger engineering organizations. Smaller teams get most of the same benefit from a DevOps engineer who treats monitoring and incident response as part of the job.
MLOps runs on the same tooling and habits as DevOps, applied to a different payload. It handles model artifacts, retraining schedules, and serving infrastructure, where a DevOps engineer handles general application code. The modeling work upstream of that pipeline is what you would hire Machine Learning Engineers for.
A DevOps engineer in the US averages roughly $134,000 a year in base pay, and the real cost to your budget lands closer to $180,000 once employer-side benefits are counted. The full picture looks like this:
That 62-day median holds across most AI Developer hiring, and DevOps sits at the competitive end of it. Market growth of 21% a year means the candidates who clear a technical screen are usually fielding several offers, which is why a traditional search stretches past the two-month mark.
Dedicated staffing fits most teams that want DevOps ownership inside their own process. The other three channels each solve a narrower problem: direct hire builds permanent headcount, freelancers handle scoped projects, and a managed service hands your infrastructure to an outside vendor.
A managed service and a DevOps consultant both work on your infrastructure from the outside, which is useful for a one-off migration or an architecture review. Dedicated staffing is the better fit when the work is continuous and you want the knowledge to stay in your team.
Screen for depth in the one cloud platform your stack runs on, then test the areas below with specifics rather than resume keywords. A strong candidate can walk you through:
These are the criteria KDCI screens against before a candidate reaches your shortlist.
Every DevOps engineer KDCI places completes an internal skills assessment that confirms deployment readiness before they reach your shortlist. The assessment covers CI/CD proficiency, infrastructure-as-code fluency, and depth in the cloud platform you run. Resume claims alone never get a candidate onto your list.
Hiring through KDCI runs from first call to start date in about two weeks. It opens with an intake conversation, where KDCI maps your stack, your pipeline tooling, and the specific ownership gaps you need filled.
From there you receive a shortlist of pre-vetted candidates matched to those requirements and interview them directly. Every candidate on that list has already cleared the deployment-readiness assessment, so the only question left on your side is which one fits your team.
For AI and ML teams, the deployment pipeline is usually what separates a model that works in a notebook from one that works in production. Someone has to own that pipeline by name, and the longer the seat stays empty, the more of your engineers' week it takes.
KDCI fills that seat with an engineer who works inside your repo, your tooling, and your process, at a cost that fits a growing team's budget. Tell us what your stack looks like and where the gaps sit, and you will have candidates to interview this week.
One engineer covers a single production environment and a handful of services. You need a second once you run multiple regions, require round-the-clock on-call, or have compliance rules that demand separation of duties.
You do. The engineer works inside your AWS, Azure, or GCP accounts using access you provision through your own identity provider, and offboarding is a matter of revoking it.
Coverage hours are agreed at intake. Tell KDCI when you deploy and what response time you need, and candidates are matched against that requirement alongside the technical screen.
Make documentation a deliverable from week one: infrastructure defined in code, runbooks for common failures, and recorded architecture decisions. Ask candidates how they documented their last environment, since the strong ones already work this way.
