Hire ML
Hire dedicated Machine Learning engineers.
ML engineers focused on getting models into production and keeping them healthy once real data starts arriving.
What You Get
What sets these engineers apart.
Training to serving without a cliff
Feature consistency between training and inference, which is where a surprising share of production ML failures originate.
Monitoring for drift
Input distribution and prediction monitoring, so degradation is detected by a system rather than by a complaint.
Reproducible experiments
Versioned data, code and parameters, so a result from three months ago can actually be reproduced.
Core Skills
What they work with.
- PyTorch and scikit-learn
- Feature engineering and feature stores
- MLflow and experiment tracking
- Model serving with TorchServe, Triton or FastAPI
- Drift detection and retraining pipelines
- Airflow and Prefect orchestration
Engagement
$3,500 / month
Per ML engineer, 160 hours per month, fully dedicated. Deployed within 48 business hours, scale up or down on two weeks' notice, full IP ownership from the first commit.
FAQ
Common questions.
Do they do research or engineering?
Engineering. Getting models deployed, monitored and retrained reliably. If you need novel model research, you want a research scientist, and we will say so.
Can they improve our existing model's performance?
Often, though the gains usually come from better features and cleaner data rather than from a different architecture. We look at the data first.
How do they handle retraining?
Scheduled or drift-triggered pipelines with evaluation gates, so a worse model cannot silently replace a better one in production.
Hire other specialists
Get In Touch
Tell us what you are building.
Stack, team size, timeline, and what success looks like. We respond within 4 business hours on business days.
Or email info@nimlogixtechnologies.com