MLOps Engineer
Turns models and pipelines into boring, reliable infrastructure.
Owns the lifecycle around models: data pipelines, training and fine-tuning workflows, deployment, versioning, monitoring, and the automation that keeps it all reproducible.
MODELS AS ROUTINE AS CODE RELEASES
WHAT THEY OWN
Concrete deliverables, not job-description poetry.
01
Reproducible ML pipelines
Data-to-deployment workflows that anyone on the team can rerun.
02
Model deployment & serving
Versioned, rollback-able model releases with canary paths.
03
Drift & quality monitoring
Alerts on data drift, output drift, and silent degradation.
04
Experiment tracking
Every training run traceable: data, params, metrics, artifacts.
05
Fine-tuning operations
Managed fine-tune workflows with eval gates before release.
06
Cost-aware scheduling
GPU and inference spend visible and optimized per workload.
PRICING
Pick the level, keep the senior oversight.
Junior
$2,300 /month
or $14/hr on Time & Material
AI-native from day one
Mid-Level
MOST HIRED$3,400 /month
or $21/hr on Time & Material
Independent feature ownership
Senior
$4,500 /month
or $28/hr on Time & Material
Architecture & judgment
Dedicated engineers are billed monthly; Time & Material is billed hourly on tracked actuals. The free trial week applies to every dedicated hire.
YOU NEED THIS ROLE IF
Models are deployed by one person, from their laptop, bravely
Nobody notices quality degrading until customers do
Training runs are unreproducible folklore
BY END OF WEEK ONE
Audited your current model path to production
Put version control around models and data
Stood up basic drift and health monitoring
Documented the one-command redeploy
OUTCOMES YOU CAN MEASURE
Model releases as routine as code releases
Degradation caught by dashboards, not customers
Reproducible experiments and audits
Visible, falling inference spend
PAIRS WELL WITH