Des Moines, IA
MLOps engineers who bridge data science and production. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to Des Moines.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Des Moines business context. Des Moines' 2025 official statement lists major regional employers across financial services and insurance, health care and education, distribution, agricultural equipment, seed manufacturing, and other manufacturing. The list supplies current operating contexts without asserting open requisitions. An MLOps Engineer owns the controls that move models from experiments into supported services. The search brief should name the training environment, registry, deployment targets, approval path, observability stack, and teams that share the platform.
Source MLOps engineers with production ownership of model pipelines and serving infrastructure
Vet on model serving, experiment tracking, feature stores, and retraining workflows
target a first candidate slate within 48 hours for qualified exclusive searches in our core disciplines after a completed intake
Permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
Start the search
Include the business context, systems, delivery phase, work model, compensation, and interview timeline. A Crosscheck search lead will use that context to calibrate the role before sourcing begins.
Local Market Brief
Separate platform construction from day-to-day model operations. Some teams need reusable pipelines and infrastructure; others need release governance, incident response, cost control, or migration from manually operated notebooks. The three sourced Des Moines contexts below turn that scope into intake and screening decisions. They do not measure current vacancies, candidate supply, or Crosscheck client activity.
Editorial market scenario
Tie each must-have requirement to a task, system, risk, or deadline. Remove industry preferences that do not change how the person will perform the job. This is planning guidance, not measured local demand.
Editorial industry scenario
A finance-facing brief should identify the transaction, reporting, audit, privacy, and availability requirements attached to the role. Confirm that this context applies to the employer before using it in the search.
Screening focus
We test for model or application ownership, evaluation discipline, data judgment, and evidence that the candidate has shipped reliable AI systems.
Published labor benchmark
BLS does not publish an occupation matching MLOps Engineer. Crosscheck uses Data Scientists (15-2051) as the closest published broad benchmark; it is not a count or pay estimate for this exact specialty.
BLS OEWS May 2025, published May 15, 2026
Published metro employment
1,240
BLS publishes a narrower metro employment estimate for the proxy occupation. Screen for adjacent experience that transfers without lowering the production bar. The estimate equals 3.074 jobs per one thousand across the metro workforce.
Employment concentration
1.82 location quotient
Des Moines-West Des Moines, IA reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$65,480 to $150,190
The metro median is 14% below the national Data Scientists median. Do not use the gap to discount niche platform or domain experience. BLS reports a $103,030 median for the proxy occupation in Des Moines-West Des Moines, IA.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Des Moines demand, clients, or candidate supply.
Sourced financial services and insurance context
A September 2025 City of Des Moines official statement lists financial-services and insurance organizations among the metropolitan area's larger employers. Require reproducible training and lineage for the local data setting. The candidate should account for code, configuration, data versions, model artifacts, and approval records without copying sensitive data into uncontrolled tools. Financial and insurance systems can connect accounts, policies, premiums, claims, payments, identity, risk rules, approvals, reconciliations, statements, reporting, and audit evidence.
Evidence to request: Ask for a pipeline diagram or repository structure that shows lineage, repeatable environments, and artifact retention. Name the account, product, policy, transaction, or claim, source ledger, money movement, identity model, control owner, reconciliation, close or reporting date, and support obligation.
Sourced health care and education context
The same Des Moines document lists hospitals, clinics, a health-benefits firm, and public education among the metropolitan area's larger employers. Define promotion between development, test, and production. Ask who approves a model, which automated checks block release, and how the team handles a failed deployment. Health and education environments may join patient or student records, appointments, benefits, billing, learning systems, workforce data, access controls, retention, and reporting.
Evidence to request: Review a release-control example with test gates, registry state, deployment strategy, and rollback steps. Set the care, benefits, teaching, or administrative workflow, authoritative record, protected data class, access reviewer, integration, reporting rule, retention requirement, and acceptance owner.
Sourced distribution and agricultural manufacturing context
Des Moines' 2025 employer table also includes distribution and data-center work, farm equipment, seed manufacturing, package shipping, agricultural tires, and other manufacturing. Set operating ownership for model services and training jobs. Include capacity, cost, feature freshness, prediction quality, and rollback signals in the interview scenario. These operations can cross product engineering, seeds or materials, production, quality, equipment, warehouses, carriers, inventory, status events, delivery, and financial settlement.
Evidence to request: Use an incident involving stale features, failed training, or degraded predictions and score the candidate's diagnosis path. Choose the product or shipment, then trace its source specification, production or receipt, quality gate, serial or lot, inventory event, carrier handoff, delivery exception, settlement, and owner.
Interview scorecard
Ask each candidate the same core questions. Score the evidence, ownership, and judgment in the answer instead of relying on job-title or keyword matches.
1. MLOps Engineer: Kubeflow
Choose a Kubeflow decision from your work as MLOps Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Des Moines.
2. ML Platform Engineer: MLflow
Describe project work you completed as ML Platform Engineer involving MLflow that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Des Moines.
3. AI Infrastructure Engineer: Metaflow
For a Metaflow system you supported, explain the handoff, operating limits, and measures used after launch. Where did your responsibility begin and end?
Use the answer to assess model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Des Moines.
Need the full MLOps Engineer evaluation guide?
The role guide covers technical scope, interview questions, and evidence checks once, without repeating the same material on every city page.
FAQ
For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake. Contract placements include a 40-billable-hour replacement guarantee, and permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
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