Last updated: July 7, 2026
MLOps Recruiters Who Know a Model in Production From a Model in a Notebook
Most recruiters read “deployed a model” and stop there. Ours ask what happened at 3 a.m. when the model quietly went stale and nobody got paged. That is the difference between a shortlist in 3 to 5 days and a req that sits open for two months.
KORE1’s MLOps recruiters source, screen, and place MLOps engineers, ML platform engineers, and ML infrastructure engineers in an average of 17 days, with 92% one-year retention, against an industry average that runs well past 60 days to fill a single production-ML seat.
What an MLOps Recruiter Actually Does
The title is barely a decade old and the market still cannot agree on what it means. That is the whole problem. One company’s MLOps engineer is a Kubernetes person who happens to deploy models. Another’s is a former data scientist who got tired of handing notebooks to an infra team that never shipped them. A recruiter who cannot tell those two apart will send you the wrong one, and you will not find out until month three.
We read the work, not the buzzwords. Show us a candidate’s history and we can tell whether they have actually owned a retraining pipeline that fires on a drift threshold, or whether they wired up one Airflow DAG for a demo and moved on. We know which staff engineers are burnt out on being the only person who understands the serving layer, and which just got a fat equity refresh and are not going anywhere. We keep a strong candidate engaged while your hiring manager disappears into a model launch for two weeks. Half of this job is timing, and the other half is knowing who is actually reachable.
None of that comes out of a keyword filter. It comes from running these searches for years, which is why this desk sits inside our broader IT recruiting practice rather than off on its own. The Bureau of Labor Statistics still has no MLOps line item and scatters the work across data scientists and software developers, where 2024 medians ran from the low $100Ks to the mid $130Ks. The 2024 Stack Overflow Developer Survey shows the people who keep production models alive are almost all employed and ignoring cold InMail, and Google’s DORA research keeps showing that reliable delivery comes down to a handful of people who can operate systems under load, not raw headcount. A general IT staffing partner cannot reach that bench cold. Someone who has lived in these conversations can.
Get an MLOps Recruiter AssignedThe Screen Most MLOps Recruiters Skip
Here is how a lot of agencies run it. They see “MLflow,” “Kubeflow,” and “SageMaker” on a resume, spot the same three words on your req, and forward it. It looks like a match. It usually is not. We inherited a search last year from a firm that screened on tool names, and the client had already interviewed four people who could name every part of a feature store and not one who could say what they do when training data and serving data quietly diverge and the model’s accuracy craters without a single error in the logs.
Our first call is technical and it is specific. Walk me through a model you shipped and then had to keep alive. How did you catch drift, and how fast. What was your rollback story the day a bad model made it to production. Did you shadow-deploy or canary, or did you just push and pray. What did inference cost before you touched it, and what did it cost after. Engineers who can answer that in their own words go to the shortlist. The ones with a certificate, a tidy GitHub, and no production scars get a warm, honest pass.
Then there is the part no job description writes down. Does this person actually enjoy making an unglamorous system reliable, or did they chase the MLOps title because it paid more than the data-science seat next to them? Can they sit between a research team that wants to ship every experiment and a platform team that wants nothing to change, and keep both talking? Are they leaving for something real, or running from a mess they will rebuild at your company by spring? Those answers are why our average lands at 17 days and not the market’s sixty-plus.
What Our MLOps Recruiters Actually Know
Not at a job-board level. At a “we know what breaks between the notebook and the pager” level. MLOps is a loop, and we screen for people who have owned every arc of it.
Pipelines & Training
Kubeflow, Airflow, Metaflow, and the feature stores like Feast and Tecton, run by people who make retraining reproducible instead of a Friday ritual.
Serving & Deployment
KServe, Seldon, BentoML, Triton, and Ray Serve. The engineers who ship a model behind a canary and roll it back before anyone files a ticket.
Monitoring & Drift
Evidently, Arize, WhyLabs, and Prometheus. The people who catch data and concept drift early, and the SRE and DevOps talent who wire the alerts that matter.
Platform & Governance
MLflow registries, SageMaker, Vertex AI, lineage, and the emerging LLMOps and ML engineering work around vector stores and RAG.
Roles Our MLOps Recruiters Fill, Repeatedly
Every line below is a search we have closed, most of them more than once. A few we run often enough that we already know who is open and who signed somewhere else last quarter before the req even reaches us. The titles keep multiplying as the field does, and LLMOps is the newest branch on the tree.
- MLOps engineers who own the path from trained model to live endpoint
- ML platform engineers building the internal tooling every data scientist depends on
- ML infrastructure engineers running GPU scheduling, Kubernetes, and serving at scale
- Senior and staff MLOps engineers who own a domain end to end
- Model deployment and inference engineers fluent in Triton, KServe, and ONNX
- ML reliability engineers who treat model health like an SLO, not an afterthought
- Feature platform engineers living between the warehouse and the model
- LLMOps engineers wrangling vector databases, RAG pipelines, and inference cost
- Cloud ML engineers on SageMaker, Vertex AI, and Azure ML
- ML observability and drift-detection specialists
- Heads of ML platform and the occasional Director of ML Engineering
How Our MLOps Recruiters Work a Search
We do not post the req and wait. The engineers you want already have a job and two recruiters in their inbox, and everything below is built around that fact.
Stack Intake, Not a Generic Brief
What is the model actually doing, and who gets hurt when it drifts. Greenfield ML platform or a system already serving traffic. Batch scoring, real-time inference, or an LLM stack. Do you need a builder, a firefighter, or someone to untangle a serving layer three people left behind? Twelve questions, twenty minutes. We do not source until that grid is full.
Shortlist in 3 to 5 Days
Three to six candidates, screened against your stack and the real failure modes, not just the keywords. Already vetted on comp, motivation, and whether they want to build platforms or babysit dashboards. Not a stack of forwarded PDFs. If the market genuinely cannot produce a strong match in that window, we tell you on day two, not day twenty.
Close Coaching Through Day 90
The offer stage is where these hires die. A surprise counter. A big-lab range that lands mid-process. An engineer weighing your platform against a flashier one with more GPUs. We stay in front of all of it. And we do not vanish at the start date. We run 30, 60, and 90-day check-ins with both sides, because retention is the number we actually get judged on.
When to Bring In an MLOps Recruiter
The Req Has Been Open Past 60 Days
Production-ML roles already take the market around two months, and every extra week the seat sits empty is a model nobody is watching and an on-call rotation stretched too thin. If your team has worked a senior MLOps search for six weeks with nothing real, the bottleneck is almost always reach. An outside recruiter with a warm bench fixes reach fast.
Your Models Ship, Then Rot
Data science can get a model to good accuracy in a notebook. Keeping it accurate in production is a different discipline, and if your models degrade a month after launch with no one owning the retraining loop, you do not have a modeling problem. You have an MLOps gap, and it is a specific person you are missing.
You Need a Build, Not a Headcount
A six-month platform stand-up. A migration off a managed service before a renewal. Sometimes the right answer is project staffing or a contract MLOps engineer, not a permanent seat, and an honest recruiter will say so instead of defaulting to a direct hire that does not fit the work.
You Cannot Tell the Real Operators Apart
Everyone interviews well now, and the resumes all list the same platforms. If your team cannot reliably separate an engineer who has carried a pager for a live model from one who has only followed a course and built a toy pipeline, that calibration is exactly what a specialist recruiter brings to the first screen.
You Are Standing Up ML in Production for the First Time
The first MLOps hire sets the patterns every model after it inherits. Sequencing that person against your data engineering and data science hires matters more than any single offer, and it is a different conversation than “send me five resumes.”
The Engineers You Want Will Not Apply
The best MLOps people are not on the boards. They are mid-migration at their current company, deep in a launch, ignoring recruiters all day. Reaching them takes relationships built over years with people who had no reason to take the call, not a fresh search the morning your req opens. That network is the whole job, and it is what you are really paying us for.
Talk to an MLOps Recruiter
Tell us what the model does, what breaks when it drifts, and the date you need someone in the seat. We will tell you honestly whether we can hit your window. Most agencies take a week to reply. We come back the same day. And because MLOps sits right where our AI, data science, and platform desks meet, when a search bleeds into modeling or infrastructure, the same team carries it under one IT staffing roof.
Common Questions
What does an MLOps recruiter do that my in-house team can’t?
A specialist MLOps recruiter brings a pre-built network of passive engineers, a technical screen run by someone who understands production ML, and close coaching through counter offers. Those are the three places internal teams usually run out of time.
Most in-house recruiting teams are excellent at general hiring. Sales, operations, marketing, that is their lane. Production-ML hiring is a narrow craft, and the passive network behind it gets built over years, not in the weeks after a req opens. We have already talked to the platform engineer who is not looking. We can tell in one call whether someone’s drift-monitoring experience is real depth or a single dashboard they set up once. This supplements your team. It does not replace it.
How much do MLOps recruiters charge?
Most contingency MLOps recruiting runs 18% to 25% of the hire’s first-year base, billed only when someone actually starts. Contract placements bill hourly with the markup built in, and senior platform or leadership searches sometimes use a retained model.
The fee is not the number that matters. The cost of the empty seat is. A senior MLOps vacancy quietly burns more than a placement fee in models drifting unwatched, launches slipping a quarter, and the occasional rushed hire who churns at month four. If you want to pressure-test the math, our guide to hiring an MLOps engineer walks through the full first-year cost. We are glad to talk through which model fits your budget before you commit to anything.
What is the difference between an MLOps recruiter and an MLOps staffing agency?
An MLOps recruiter is the person who runs your search. A staffing agency is the operation around them: engagement models, compliance, payrolling, and a deeper bench. KORE1 is both, so the recruiter on your req is backed by 20-plus years of infrastructure founded in 2005.
If you want to know who picks up the phone and works your search, that is the recruiter, and that is what this page is about. If you want the full menu of how we engage, our MLOps engineer staffing page covers contract, contract-to-hire, direct hire, and managed teams in detail. Same desk behind both pages. We just split them so the people do not get buried under the process.
How do MLOps recruiters find candidates?
The good ones do not start with a job posting. They start with a network of MLOps and ML platform engineers they already know, built over years of staying in touch with people who are not looking. Boards and InMail come second, only to widen a search the network already started.
Here is the part most clients never see. By the time your req reaches us, a good chunk of the sourcing is already done, because we have been talking to platform, infrastructure, and reliability people all year, not just the week you called. It is also why we can be honest early. If a role is genuinely thin, say a Triton-and-GPU-scheduling specialist in a small market, we will tell you on day two from real signal, not a sales script.
Is an MLOps engineer the same as an ML engineer or a DevOps engineer?
No, though the lines blur. An ML engineer usually builds and trains the models. A DevOps engineer runs general infrastructure. An MLOps engineer sits in between, owning the reliability of models once they are live: deployment, monitoring, drift, and retraining.
This is exactly where bad hires happen. A company posts for “MLOps” and interviews DevOps engineers who have never seen model drift, or data scientists who have never carried a pager. All three are real skill sets, and the right mix depends on what you already have. Part of our screen is figuring out which one you actually need, which is why our ML engineering and DevOps recruiting desks are one call away when a search turns out to be adjacent.
How long does it take to hire an MLOps engineer?
First shortlist in 3 to 5 business days. Average hire in 17 days across our recent technical placements, against an industry average that runs past 60 days for production-ML roles and longer for staff-level platform seats.
Speed comes from relationships, not InMail volume. We are not starting from zero when you call, so the first strong names usually move fast. It also means we can be straight when a role needs a longer runway. A staff engineer who has owned real-time inference at scale is not a three-day shortlist, and we would rather say that than waste a week pretending otherwise. If you are still scoping comp, the MLOps engineer salary guide is a good place to set the band first.
Do your MLOps recruiters handle contract, contract-to-hire, and direct hire?
Yes, all three. Contract for platform builds, migrations, and surge work. Contract-to-hire for higher-risk roles where a trial period lowers the cost of a wrong call. Direct hire for core team members and leadership.
The model should follow the work, not the other way around. A four-month platform migration does not need a permanent hire. A founding MLOps engineer on a team that is finally taking models seriously almost certainly does. If you ask for a structure that does not fit the work, expect us to push back. Usually we are right, and it is far cheaper than finding the mismatch four months into a contract that should have been a direct hire from the start. For longer builds, project staffing often beats a string of one-off contracts.