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MLOps Engineer
Keep AI systems stable, deployed, monitored, and ready to serve real users without crashing.
About this role
MLOps Engineers help AI systems run smoothly after they are built. They support deployment, monitoring, reliability, cloud systems, and the behind-the-scenes structure that keeps AI tools from breaking.
Where it is heading by 2030
As more organizations depend on AI, the need for people who can keep systems stable, secure, and scalable will keep rising. This path fits students who like systems, troubleshooting, and structure.
Salary exploration range
Broad U.S. exploration range: about $85K-$180K+, depending on cloud, DevOps, software, and machine learning operations experience.
Student outcome
Students see how infrastructure keeps AI tools reliable after they leave the lab.
Skills to start learning
Cloud basics
Linux and command line
Docker concepts
Kubernetes awareness
Monitoring
System design
Model deployment basics
Ways to gain entry
Learn command line and computer systems basics
Practice coding and debugging
Explore cloud and server concepts
Build small deployment projects
Grow into IT, cloud, DevOps, or AI infrastructure pathways
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