AI & ML
Hire senior Amazon SageMaker engineers
SageMaker is where AWS-native teams productionize models. Our MLOps engineers build the full loop: pipelines, feature stores, model registry, endpoints with autoscaling, and the monitoring that notices drift before your metrics do.
Shortlist of around 3 senior profiles in under 5 business days
- Seniority floor
- 7+ years owning production systems
- Time zone
- Full overlap with US Eastern to Pacific
- Shortlist
- Around 3 curated profiles in under 5 business days
- Engagement model
- Embedded in your team, no resellers or middlemen
- Pricing
- All-inclusive rate, around 35% below the US onshore equivalent
How we vet
What senior Amazon SageMaker actually looks like
SageMaker Pipelines for reproducible training workflows
Endpoint design: instance selection, autoscaling, and multi-model endpoints for cost
Model Registry governance and approval workflows
Drift and quality monitoring with Model Monitor and Clarify
The roles that master Amazon SageMaker
Amazon SageMaker questions, answered
SageMaker or a custom Kubernetes ML stack?
For AWS-native teams, SageMaker removes platform toil. Seniors recommend custom stacks only when portability or GPU economics demand it.
Can they lower our SageMaker bill?
Yes. Endpoint right-sizing, multi-model endpoints and spot training typically cut costs significantly.
Do they handle the full path from notebook to endpoint?
Yes. That full path, plus monitoring and retraining triggers, is exactly what senior MLOps means.
Usually paired with Amazon SageMaker
Keep reading about hiring for Amazon SageMaker
Need senior Amazon SageMaker on your team?
Tell us the context. A curated shortlist of senior profiles in under five business days, working in your time zone.