Dallas, TX
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 Dallas.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Dallas business context. Dallas Economic Development lists finance, AI and semiconductors, data centers, advanced manufacturing, aerospace, and logistics among its target industries. The mix creates several valid technical hiring scenarios, each with different operating evidence. 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 Dallas 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
Set the system boundary, decision rights, work model, and interview schedule before sourcing. Candidates can then compare the role on concrete responsibilities. 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
10,120
BLS publishes a sizable metro employment estimate for the proxy occupation. The intake still needs to isolate the platform, delivery stage, and ownership required here. The estimate equals 2.498 jobs per one thousand across the metro workforce.
Employment concentration
1.48 location quotient
Dallas-Fort Worth-Arlington, TX reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$65,190 to $173,340
The metro median is 6% above the national Data Scientists median. Test whether the role's scope and location requirement support that difference. BLS reports a $127,750 median for the proxy occupation in Dallas-Fort Worth-Arlington, TX.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Dallas demand, clients, or candidate supply.
Sourced financial services and fintech context
Dallas Economic Development lists financial services and fintech among the sectors it targets for growth and recruitment. Its industry material also describes Dallas as a major financial employment center, which supports a finance-systems scenario without proving role-level demand. 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. Finance platforms require controlled access, complete transaction records, reconciliations, and releases that respect reporting calendars.
Evidence to request: Ask for a pipeline diagram or repository structure that shows lineage, repeatable environments, and artifact retention. State the product, accounting or risk process, control owner, and close or reporting deadline attached to the role.
Sourced ai, semiconductors, and data centers context
Dallas Economic Development groups AI, semiconductors, and data centers within its technology targets. Those activities span software, physical infrastructure, capacity planning, and systems that support design or production work. 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. Compute-heavy environments can impose capacity limits, hardware dependencies, energy constraints, and maintenance windows that shape software design.
Evidence to request: Review a release-control example with test gates, registry state, deployment strategy, and rollback steps. Separate experience running cloud software from experience with data-center operations, semiconductor workflows, or systems close to physical equipment.
Sourced aerospace, manufacturing, and logistics context
Dallas Economic Development also targets advanced manufacturing, aviation, defense, aerospace, transportation, and logistics. A technical search in that setting may support physical assets, regulated supply chains, warehouses, or field operations. Set operating ownership for model services and training jobs. Include capacity, cost, feature freshness, prediction quality, and rollback signals in the interview scenario. Asset-based operations can require serial traceability, supplier integration, controlled maintenance records, and support across several facilities.
Evidence to request: Use an incident involving stale features, failed training, or degraded predictions and score the candidate's diagnosis path. Define whether the candidate needs sector knowledge, site experience, export-control awareness, or a record of supporting distributed operations.
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 Dallas.
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 Dallas.
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 Dallas.
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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