New York, NY
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 New York.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced New York business context. NYCEDC identifies technology activity alongside finance, insurance, health care, media, and commerce. Those anchor industries create distinct system requirements and give hiring teams a better starting point than a generic New York technology label. 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 New York 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 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
23,160
BLS publishes a large metro employment estimate for the proxy occupation, but the figure covers many employers, seniority levels, and specializations outside MLOps Engineer work. The estimate equals 2.440 jobs per one thousand across the metro workforce.
Employment concentration
1.45 location quotient
New York-Newark-Jersey City, NY-NJ reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$79,870 to $216,030
The metro median is 13% above the national Data Scientists median. Test whether the role's scope and location requirement support that difference. BLS reports a $135,980 median for the proxy occupation in New York-Newark-Jersey City, NY-NJ.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of New York demand, clients, or candidate supply.
Sourced financial services context
NYCEDC describes New York as a global financial-services center spanning banking, securities, investment, and fintech. Its current industry page connects the finance sector with enterprise software, cloud computing, and financial technology investment. 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 products can require low error tolerance, complete audit records, controlled deployments, and coordination with risk or compliance teams.
Evidence to request: Ask for a pipeline diagram or repository structure that shows lineage, repeatable environments, and artifact retention. Identify the financial product, transaction path, control framework, and production support window before screening candidates.
Sourced health care and insurance context
NYCEDC's emerging-technology profile lists health care and insurance among the city's anchor industries. Those sectors support technical roles tied to member, patient, claims, billing, research, or internal workforce systems. 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 insurance systems can combine sensitive data, rules-driven workflows, vendor interfaces, and evidence retained for review.
Evidence to request: Review a release-control example with test gates, registry state, deployment strategy, and rollback steps. Name the record type, regulatory boundary, business owner, and exception process that the hire will support.
Sourced media, retail, and commerce context
NYCEDC also identifies media, fashion, retail, and manufacturing among New York's anchor industries. Technical teams in that setting may support content rights, customer identity, inventory, orders, advertising, or digital product delivery. Set operating ownership for model services and training jobs. Include capacity, cost, feature freshness, prediction quality, and rollback signals in the interview scenario. Customer-facing systems can face seasonal volume, rapid release cycles, third-party services, and data use rules that differ by product.
Evidence to request: Use an incident involving stale features, failed training, or degraded predictions and score the candidate's diagnosis path. Define the traffic pattern, customer data boundary, content or order lifecycle, and revenue-critical events the candidate must have handled.
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 New York.
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 New York.
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 New York.
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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