San Antonio, 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 San Antonio.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced San Antonio business context. San Antonio's 2022 economic-development framework separates mobility, IT security and infrastructure, and bioscience from other strategic clusters. Those categories support hiring briefs tied to production systems, controlled technology, and regulated research or care. 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 San Antonio 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
Give candidates the integration, approval, audit, and change-management boundaries during the interview. Ask for comparable decisions from prior work. This is planning guidance, not measured local demand.
Editorial industry scenario
A public-sector brief should identify access, procurement, documentation, security, and stakeholder constraints before sourcing begins. 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
1,730
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 1.539 jobs per one thousand across the metro workforce.
Employment concentration
0.91 location quotient
San Antonio-New Braunfels, TX sits near the national employment concentration for this proxy occupation. Use role evidence and work-model requirements to set the sourcing radius.
Annual wage reference
$59,790 to $165,250
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 $103,290 median for the proxy occupation in San Antonio-New Braunfels, 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 San Antonio demand, clients, or candidate supply.
Sourced mobility context
The City of San Antonio's economic-development framework defines mobility through aerospace, automotive, logistics, and manufacturing activity. 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. Mobility programs may connect engineering definitions, vehicles or aircraft, parts, plants, quality, maintenance, suppliers, warehouses, field assets, service, and cost records.
Evidence to request: Ask for a pipeline diagram or repository structure that shows lineage, repeatable environments, and artifact retention. Set the product and program boundary, configuration baseline, facility, production method, traceability rule, quality release, logistics handoff, service model, and change authority.
Sourced it security and infrastructure context
San Antonio's framework identifies IT security and infrastructure as a strategic cluster connected to cybersecurity, technology, aerospace, defense, and military operations. 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. Secure infrastructure work can involve identity, cloud and hosted services, networks, classified or controlled data, monitoring, incident response, change records, and continuity duties.
Evidence to request: Review a release-control example with test gates, registry state, deployment strategy, and rollback steps. Name the service, data classification, identity authority, trust boundary, control framework, alert and response path, evidence retention, recovery objective, and approving security owner.
Sourced bioscience anchors and catalysts context
The San Antonio framework defines a bioscience cluster that includes health care, higher education, research and development, and defense or military medicine. Set operating ownership for model services and training jobs. Include capacity, cost, feature freshness, prediction quality, and rollback signals in the interview scenario. Bioscience and medical work may join patient care, research data, specimens, devices, laboratories, validated products, access controls, billing, workforce, and quality review.
Evidence to request: Use an incident involving stale features, failed training, or degraded predictions and score the candidate's diagnosis path. Define the care, study, product, or enterprise process, regulated boundary, source record, validation protocol, device or lab interface, access rules, retention, and reviewer.
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 approved data use, evaluation records, human oversight, deployment boundaries, and security review. The public-sector and security work context is an editorial scenario, not a measured claim about San Antonio.
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 approved data use, evaluation records, human oversight, deployment boundaries, and security review. The public-sector and security work context is an editorial scenario, not a measured claim about San Antonio.
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 approved data use, evaluation records, human oversight, deployment boundaries, and security review. The public-sector and security work context is an editorial scenario, not a measured claim about San Antonio.
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.
Continue your research