MLOps Engineer
Builds CI/CD pipelines for models, automates retraining workflows, and maintains deployment infrastructure for production ML.
AI/ML & LLM Engineering
MLOps Engineer staffing across the US and Canada
MLOps engineers who bridge data science and production. Choose a market below for local hiring context and the screening priorities Crosscheck uses for this role.
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.
Role-specific recruiting
What a focused MLOps Engineer search covers
A successful search starts with the outcomes this person must own, the environment they will inherit, and the evidence that separates production experience from keyword familiarity. Crosscheck aligns those requirements during intake, then screens a specialist network against the agreed role, compensation, location, and interview process.
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.
Role coverage
Related MLOps Engineer profiles
The exact title varies by team structure and project stage. These are the adjacent profiles commonly considered during intake and technical screening.
ML Platform Engineer
Designs and operates internal ML platforms, model registries, feature stores, experiment tracking, and self-service tooling for data scientists.
AI Infrastructure Engineer
GPU cluster management, inference cost optimization, and cloud-native compute orchestration for large-scale AI workloads.
Data Platform Engineer
Data pipelines, lakehouse architecture, and feature engineering infrastructure that feed ML training and serving systems.
ML SRE
Production reliability for ML systems, SLA management, latency monitoring, incident response, and capacity planning for inference workloads.
Model Deployment Engineer
Specializes in packaging, containerizing, and deploying models to cloud and edge environments with dependable version control and rollback.
Technical and functional screening scope
The intake identifies which areas are essential on day one and which can be adjacent experience. Screening then focuses on decisions made, systems shipped, constraints handled, and measurable outcomes.
Orchestration & Pipelines
Kubeflow · MLflow · Metaflow · Airflow · Prefect / Dagster
Model Serving
Seldon Core · BentoML · NVIDIA Triton · TorchServe · Ray Serve
Cloud ML Platforms
AWS SageMaker · Google Vertex AI · Azure ML · Databricks MLflow · Weights & Biases
Feature Stores
Feast · Tecton · Hopsworks · AWS Feature Store · Redis (online serving)
Infrastructure & GitOps
Docker / Kubernetes · Terraform / Pulumi · ArgoCD · Helm · GitHub Actions
Monitoring & Observability
Grafana / Prometheus · Evidently AI · WhyLabs · Datadog ML · OpenTelemetry
Interview calibration
How to evaluate MLOps Engineer experience
The search team uses the completed brief to separate adjacent familiarity from work the candidate owned. Each interviewer should use the same scenario, record the evidence provided, and score the answer against the responsibilities agreed during intake.
MLOps Engineer
Builds CI/CD pipelines for models, automates retraining workflows, and maintains deployment infrastructure for production ML.
Interview prompt: Walk through a project where you owned this work. Explain the decisions you made, what you implemented and how you checked the result.
ML Platform Engineer
Designs and operates internal ML platforms, model registries, feature stores, experiment tracking, and self-service tooling for data scientists.
Interview prompt: Walk through a project where you owned this work. Explain the decisions you made, what you implemented and how you checked the result.
AI Infrastructure Engineer
GPU cluster management, inference cost optimization, and cloud-native compute orchestration for large-scale AI workloads.
Interview prompt: Walk through a project where you owned this work. Explain the decisions you made, what you implemented and how you checked the result.
Market directory
Choose where you need to hire.
Use a quick link or choose a state or province. Every market opens a city-specific MLOps Engineer hiring guide.
Quick market links
United States by state
Open states with multiple cities
Alabama (AL)BirminghamArkansas (AR)FayettevilleConnecticut (CT)HartfordDistrict of Columbia (DC)WashingtonGeorgia (GA)AtlantaIdaho (ID)BoiseIllinois (IL)ChicagoIndiana (IN)IndianapolisIowa (IA)Des MoinesKansas (KS)WichitaKentucky (KY)LouisvilleMaryland (MD)BaltimoreMassachusetts (MA)BostonMinnesota (MN)MinneapolisNebraska (NE)OmahaNevada (NV)Las VegasNew Mexico (NM)AlbuquerqueOregon (OR)PortlandRhode Island (RI)ProvidenceUtah (UT)Salt Lake CityVirginia (VA)Richmond
California (CA)5 cities
Colorado (CO)2 cities
Florida (FL)4 cities
Louisiana (LA)2 cities
Michigan (MI)2 cities
Missouri (MO)2 cities
North Carolina (NC)4 cities
Ohio (OH)3 cities
Oklahoma (OK)2 cities
Pennsylvania (PA)2 cities
South Carolina (SC)2 cities
Tennessee (TN)4 cities
Texas (TX)5 cities
Canada by province
Choose a province to open its markets
Need a wider or fully remote search?
The market pages are planning guides, not claims of a physical office in every city. Crosscheck recruits across the United States and Canada from Denver. For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake.
Discuss the search