AI Ops / MLOps
Deploy, monitor and keep AI systems reliable in production. If you've got an infrastructure or DevOps background, this is one of the fastest-growing and most natural ways to move into AI.
Typical pay: €55k–€90k (IE) · £48k–£80k (UK) estimate — verify before relying on it
The short version
Everyone's building AI apps; far fewer people can keep them running reliably, affordably and fast in production. AI Ops / MLOps is that role. If you've got an infrastructure, DevOps or backend background, this is one of the most natural — and fastest-growing — ways to move into AI.
A day in the life
You're deploying an LLM service and making sure it scales when traffic spikes. You set up monitoring so you find out a model's misbehaving before customers do. You shave latency and cost without breaking quality, and you build eval pipelines that flag regressions before they ship. It's the discipline of reliability, applied to AI systems.
Who it suits
This is a great fit if you come from DevOps, infrastructure, sysadmin or backend work, and you care more about keeping systems healthy than shipping features. Your existing skills transfer directly — you're mostly adding the AI-specific layer on top.
Is this you?
Find your path → to check the fit, or talk to a mentor → to plan the move from your current stack.
An honest note. IE/UK salary and demand figures are realistic estimates, not guarantees. This path is most accessible if you already have infra or DevOps experience to build on.
Your roadmap
Foundations
Confirm the platform base.
- Solid Python
- Docker and containers
- CI/CD pipelines
- Cloud basics
Core skills
Learn to run AI in production.
- Model serving and deployment
- Monitoring and observability for LLM systems
- Cost and latency optimisation
- Vector databases in production
- Evaluation pipelines that catch regressions
Portfolio
Prove you can run it reliably.
- Deploy and monitor a real LLM app
- Add logging, evals and autoscaling
Job-ready
Target MLOps and AI-platform roles.
- Target MLOps, AI-platform and AI-infra roles
- Lead with reliability and your existing infra experience
- Show you can keep AI systems up, fast and affordable
What you'll need to start
- Infrastructure, DevOps, sysadmin or backend experience
- Comfort with the command line and automation
- Programming ability (Python especially)
Related paths
AI Application Developer
Build real software on top of LLM APIs — chatbots, RAG assistants, tool-using agents. The most in-demand technical entry into AI right now, and one where a strong portfolio often beats a formal ML background.
ML / Data Practitioner
The deeper data-science and machine-learning route — the longest path here, with the highest ceiling. For people who like maths, data and patience, and want to build models, not just use them.