Austin, 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 Austin.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Austin business context. Austin's May 2026 economic-development policy draft names specific growth sectors and infrastructure programs. The document supports hiring scenarios tied to semiconductors, health innovation, and major public infrastructure without claiming that a named employer has an open role. 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 Austin 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 cross-industry brief should start with the systems, users, risks, and outcomes behind the job title. 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
3,730
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 2.882 jobs per one thousand across the metro workforce.
Employment concentration
1.71 location quotient
Austin-Round Rock-San Marcos, TX reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$74,640 to $186,010
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 $127,360 median for the proxy occupation in Austin-Round Rock-San Marcos, 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 Austin demand, clients, or candidate supply.
Sourced semiconductors and microelectronics context
The City of Austin's May 2026 policy draft identifies semiconductors and microelectronics as a target sector and links the sector to local research, state programs, and federal investment. The document names design, manufacturing, and supply-chain activity as parts of the cluster. 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. Semiconductor work can combine factory systems, engineering data, long equipment lifecycles, and strict production change windows.
Evidence to request: Ask for a pipeline diagram or repository structure that shows lineage, repeatable environments, and artifact retention. Separate experience with corporate software from experience inside design, test, fabrication, or equipment operations.
Sourced life sciences and health innovation context
Austin's 2026 policy draft lists life sciences and health innovation as a target sector. It points to diagnostics, biotechnology, health technology, and the research and clinical institutions that support those activities. 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. Research and clinical products can require reproducible analysis, controlled data access, validation records, and review by nontechnical subject experts.
Evidence to request: Review a release-control example with test gates, registry state, deployment strategy, and rollback steps. Document whether the role supports research, a regulated product, clinical operations, or an internal business system because each path changes the proof required.
Sourced mobility and infrastructure technology context
The Austin policy draft treats mobility and infrastructure technology as a sector connected to I-35, Project Connect, airport expansion, and water infrastructure. It describes a regional investment cycle with construction, technology, utilities, and smart-city work. Set operating ownership for model services and training jobs. Include capacity, cost, feature freshness, prediction quality, and rollback signals in the interview scenario. Infrastructure programs join field schedules, public procurement, asset data, and systems that must remain available during phased delivery.
Evidence to request: Use an incident involving stale features, failed training, or degraded predictions and score the candidate's diagnosis path. Identify the asset, operating agency, implementation phase, and outage tolerance before deciding whether industry experience is required.
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 the model or application, evaluation method, input data, production limits, and owner after launch. The cross-industry technical work context is an editorial scenario, not a measured claim about Austin.
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 the model or application, evaluation method, input data, production limits, and owner after launch. The cross-industry technical work context is an editorial scenario, not a measured claim about Austin.
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 the model or application, evaluation method, input data, production limits, and owner after launch. The cross-industry technical work context is an editorial scenario, not a measured claim about Austin.
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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