Hire MLOps engineers who keep AI running.
The infrastructure people behind reliable AI: pipelines, evals, observability, and the cost and latency work that decides whether a clever model becomes something your company can actually run on.
At a glance
The infra people who keep AI fast, efficient, and observable in production.
Review the work MLOps & AI Infra Engineers can own, the skills required, and the systems they can help you build.
- Who this is for
- Engineering leaders who need an engineer to work within their existing team.
- Topics
- Cloudflare
- AWS
- Kubernetes
- Docker
- CI/CD
- Observability
What they own.
- Build CI/CD and deployment pipelines for models and prompts
- Stand up evals and observability so quality is visible
- Tune latency and cost across the whole AI stack
- Scale vector and inference infrastructure
- Set up safe rollout, rollback, and versioning
- Run on-call-ready systems with real alerting
What you can ship with them.
Tools they reach for in production.
- Infra
- CloudflareAWSKubernetesDocker
- Ops
- CI/CDObservabilityTracingLangfuseOpenTelemetry
- Serve
- vLLMEdge inferenceCaching
- Stack
- PythonTerraformGrafana
Seniority: Engineers who have carried a pager for production AI, not just built it.
Five stages.
The top 3% remain.
Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.
400 applicants
3%of applicants reach
your shortlist
What removes them
We start with something they built
100% → 20%One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
We break something and watch them fix it
20% → 9%A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
How will you know it is working?
9% → 5%Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
Make it fast without running up the bill
5% → 4%We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
It is late and the AI got it wrong
4% → 3%What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
Often hired together.
Data Engineers for AI
The pipelines that turn raw data into retrieval-ready, training-ready fuel.
AI Agent Engineers
Builders of agents that plan and take real actions in your systems, safely.
LLM Engineers
Model specialists who fine-tune, quantise, and serve the model itself.
Find the people to accelerate your roadmap.
You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.