AI & Machine Learning
Hire MLOps Engineers
Engineers who make model delivery boring — versioned, reproducible, monitored and reversible.
MLOps earns its place the second time a model is deployed. The first deployment can be done by hand. The tenth, with a rollback at two in the morning and a question about which data version produced the current predictions, cannot. The discipline is mostly about making the boring path the easy one.
When hiring, ask what happens after a bad model reaches production. A candidate who has lived through it will describe the registry, the promotion gate that should have caught it and how long rollback actually took. One who has not will describe a training pipeline. Look also for comfort with ordinary platform engineering — containers, CI and infrastructure as code — because most of this role is that, applied to models.
What these engineers do
- Reproducible training runs with MLflow or Weights and Biases and pinned data versions
- Model registries, staged promotion between environments and one-command rollback
- Containerised serving on Kubernetes with autoscaling and GPU scheduling where needed
- Drift, latency and inference cost monitoring wired to alerts somebody actually owns
- Evaluation gates in CI that can block a promotion, not merely record a number
Delivered AI-first
AI assistance is used to generate the infrastructure surface around a model - Dockerfiles, Helm charts, pipeline definitions, Terraform modules and the glue between a registry and a serving layer. That code is reviewed exactly as application code is, because a broken rollback path is discovered at the worst possible moment. Nothing is promoted on an assistant's recommendation; promotion stays gated on evaluation runs and on a human approving the diff. The measurable effect is throughput per engineer, not fewer reviews.