MLOps Engineer
An MLOps engineer gets machine-learning models into production and keeps them healthy there.
- Package and serve models
- Build training and deployment pipelines
- Monitor models and data drift
- Run workloads on containers and Kubernetes
- A served model behind an API
- A training pipeline
- Monitoring for a deployed model
Where it leads: Senior MLOps, platform engineering, or ML infrastructure.
Every skill, at the level the job needs
Updating… · change the level or company to see how it shifts
docker
core- Virtualization and containerization
- Package managers
- File modes and permissions
- Docker
- Command line
- Walking through directories in the command line
git-github
core- git init
- What is Version Control?
- Why use Version Control?
- git config
- Local vs Global Config
- Git
mlops
core- Python
- Bash
- Git
- Git commands
- GitHub
- Starting with GitHub
datastructures-and-algorithms
core- Language Syntax
- Control Structures
- Python
- Pseudocode
- Pseudocode basics
- Functions
What the levels mean
- L1 · AwareKnows what it is and when it's used
- L2 · BasicsCan use it with guidance
- L3 · WorkingUses it independently on the job
- L4 · StrongHandles hard cases, reviews others
- L5 · ExpertDesigns with it, teaches it
Interviews, by kind of company
Your route adds an interview track that matches the company you choose.
Large IT services firms: aptitude, fundamentals, technical and HR rounds.
Online assessment, coding rounds, a design round.
Hard timed coding rounds, system design, behavioural interviews.
Questions
What does a MLOps Engineer need to know?
Docker Roadmap, MLOps Roadmap at a working level, plus Kubernetes Roadmap, Machine Learning, Python Developer. The skills section above lists every one with its level.
How long does it take to become a MLOps Engineer?
About 139 hours of study from scratch. At 10 hours a week that's roughly 14 weeks; anything you already know is skipped after the readiness test.
I already know some of this. Do I start from zero?
No. The optional readiness test finds what you already know and your route skips it.
Does the route change for product companies or senior roles?
Yes. Choose your level and the kind of company above: the skills and target levels adjust, for example deeper data structures and system design for product companies.
Other roles in the Data & AI track
They share many skills with this one, so progress carries over if you switch.
Answer questions with SQL, Python and dashboards.
Models and analyses with Python and machine learning.
Pipelines and data platforms.
Applications built on AI models: assistants, agents, AI features.