MLOps and ML Infrastructure Jobs
MLOps Engineers bridge the gap between data science and production systems. They build the platforms, pipelines, and tooling that enable teams to train, deploy, and monitor machine learning models reliably at scale.
Last updated: May 13, 2026
Latest MLOps Jobs
View all jobsSenior Automation & AI Platform Engineer
Data Science & MLOps Specialist (m/f/d)
AI Infrastructure Engineer
Junior MLOps Engineer
Data Scientist Senior (Machine Learning, MLOps & IA Générative) - Lille (H/F)
MLOps Engineer
MLOps Engineer
MLOps Engineer - Energy AI Platform
MLOPS Engineer
Senior ML Platform / ML Infrastructure Engineer II
MLOps Engineer / AI Operations Specialist
Senior Principal MLOps Engineer, AI Inference
MLOps Engineer
Senior ML Ops Engineer (Machine Learning Infrastructure)
MLOps Engineer
Senior Automation & AI Platform Engineer
Data and MLOps Engineer
MLOps Engineer
MLOps Engineer
Senior MLOps Engineer - Football Metrics
Frequently Asked Questions
What is MLOps?
MLOps (Machine Learning Operations) applies DevOps principles to ML systems, covering model versioning, automated training pipelines, model serving, monitoring, and infrastructure management. The MLOps market is projected to grow from $3.8 billion to $21.1 billion by 2026, reflecting the critical need for production ML infrastructure. The field is also evolving into LLMOps, which focuses specifically on managing and deploying large language models at scale. MLOps Engineers earn between $132K-$199K, with senior roles commanding $160K-$220K+, making it one of the best-compensated AI specializations.
What tools do MLOps Engineers use?
Core MLOps tools include Kubernetes, Docker, MLflow, Kubeflow, Airflow, Terraform, and cloud ML services like SageMaker and Vertex AI. Strong knowledge of CI/CD pipelines and infrastructure-as-code is essential for automating model deployment and monitoring. As the field evolves toward LLMOps, familiarity with tools for managing large language model inference, prompt versioning, and GPU orchestration is becoming increasingly valuable. The MLOps market's explosive growth from $3.8B to a projected $21.1B by 2026 means new tools and platforms emerge regularly, so continuous learning is part of the role.
What is the career path for MLOps Engineers?
MLOps Engineers typically start from either a DevOps/SRE background or a data engineering background, gradually specializing in ML infrastructure. Junior MLOps roles start around $132K, progressing to senior positions at $160K-$220K+ as you gain expertise in production ML systems. The career path often leads to Staff or Principal ML Platform Engineer roles, or into engineering management for ML infrastructure teams. With the MLOps market projected to reach $21.1 billion by 2026, career growth opportunities are substantial, and the emerging LLMOps specialization offers an additional avenue for advancement.
AI Job Insights for MLOps Jobs
Salary Range (Yearly, USD)
$45K - $400K
Median $172K from 56 listings with salary data
Top Companies Hiring
Based on recent listings shown on this page.
Common Roles
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