MLOps Jobs – Remote & On-Site Machine Learning Operations Roles
Find your next MLOps engineer role. Browse remote MLOps jobs and on-site positions building ML pipelines, model serving infrastructure, and production AI systems.
MLOps engineers are the backbone of production machine learning, ensuring AI models are deployed, monitored, and maintained reliably at scale. Our curated MLOps job listings feature opportunities working with ML platforms like Kubeflow, MLflow, and SageMaker, along with model serving frameworks and feature stores. Whether you're looking for remote MLOps jobs, senior ML infrastructure engineer roles, or cloud MLOps careers with AWS, Azure, or GCP, CloudOpsJobs connects you with companies building the next generation of AI-powered products. Explore positions in model deployment, training pipeline automation, and machine learning platform engineering.
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53 positions
About MLOps Careers
MLOps (Machine Learning Operations) roles focus on deploying, monitoring, and maintaining machine learning models in production. Work with ML pipelines, model serving, feature stores, and the infrastructure powering AI systems at scale.
Common Skills & Tools
- • ML Platforms: Kubeflow, MLflow, SageMaker, Vertex AI
- • Model Serving: TensorFlow Serving, Seldon, KServe
- • Feature Stores: Feast, Tecton, Hopsworks
- • Orchestration: Airflow, Prefect, Dagster
- • Languages: Python, SQL, Spark, PyTorch/TensorFlow
- • Infrastructure: Kubernetes, Docker, GPU clusters
MLOps salary ranges
| Level | Range |
|---|---|
| Mid-level | $130k – $175k |
| Senior | $155k – $220k |
| Staff / Principal | $185k – $270k+ |
Sources: Levels.fyi, Glassdoor, LinkedIn Salary Insights (aggregated US base salary ranges, 2025–2026).
Demand & growth
MLOps roles are in high demand as companies move ML models from research to production. Demand for ML infrastructure and production AI systems has grown sharply with adoption of large-scale AI and LLM deployments.
Certifications
- AWS Certified Machine Learning – SpecialtyValidates design and implementation of ML solutions on AWS.
- Google Professional ML EngineerCovers deploying and maintaining ML models on GCP.
- Azure AI Engineer AssociateMicrosoft certification for building and managing AI solutions on Azure.