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MLOps Engineer

An MLOps Engineer builds the infrastructure and automation that lets machine learning run reliably in production. They create pipelines for training, deployment, versioning, and monitoring, and treat models and data as first-class software artifacts. The role applies DevOps discipline to ML, reducing the gap between a working notebook and a dependable live system.

Responsibilities

Build CI/CD pipelines for training and deploying models
Automate model versioning, packaging, and rollout
Set up monitoring for performance, drift, and data quality
Manage feature stores and model registries
Scale training and serving infrastructure on the cloud
Improve reproducibility and reliability of ML workflows

Must-have skills

Strong DevOps skills: CI/CD, containers, infrastructure as code
Cloud platform experience (AWS, GCP, or Azure)
Kubernetes and orchestration
Python and ML workflow familiarity
Monitoring and observability practices

Nice-to-have skills

MLOps platforms (Kubeflow, MLflow, SageMaker)
Feature store and model registry tooling
GPU cluster management
Data engineering experience

Average Salary

Typical US base salary for an HR Generalist by experience level.

Junior

0–2 yrs experience

$115,000 – $145,000
US annual salary (USD)

Intermediate

0–2 yrs experience

$145,000 – $185,000

US annual salary (USD)

Senior

0–2 yrs experience

$185,000 – $240,000

US annual salary (USD)

Figures are annual US market estimates for orientation, not offers. Actual pay varies by location, company stage, and equity.

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Frequently asked questions

What is MLOps in simple terms?
MLOps is the practice of shipping and maintaining machine learning in production reliably, using automation for training, deployment, and monitoring, much like DevOps does for regular software.
Is MLOps the same as DevOps?
It shares principles with DevOps but adds concerns unique to ML: data versioning, model drift, retraining, and evaluation. Many MLOps engineers come from a DevOps background.
What skills matter most for MLOps?
Cloud infrastructure, containers and Kubernetes, CI/CD, scripting, and enough ML knowledge to understand model lifecycles and failure modes.
Why do companies need MLOps engineers?
Because most models fail not in research but in deployment and upkeep. MLOps engineers make ML dependable, observable, and cost-efficient at scale.

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