Production ML Engineer: Scale Models with MLOps & KPI

SoftRevery; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

SoftRevery is seeking an ML Engineer to take models from notebook to production and keep them healthy at scale. You will build training and evaluation pipelines, deploy, monitor, and retrain models with a modern MLOps stack. Work with data and business metrics to ship models that measurably move KPIs.

Design experiments, optimize latency and cost, and implement CI/CD for ML systems in the cloud. Proficiency in Python and PyTorch (or TensorFlow) is required, with cloud experience (AWS, GCP or

Qualifications

• 3+ years in ML engineering or applied data science in production.
• Strong Python and PyTorch (or TensorFlow); solid ML fundamentals.
• Hands-on MLOps: pipelines, model registry, monitoring, CI/CD.
• Cloud experience (AWS, GCP, or Azure) and containerization.

Responsibilities

• Build training and evaluation pipelines for ML and generative AI models.
• Deploy, monitor, and retrain models with a modern MLOps stack.
• Design experiments and define offline/online metrics with stakeholders.
• Optimize inference for latency and cost in production.

Skills

Python

PyTorch

TensorFlow

MLOps

Cloud platforms

Tools

Docker

Kubernetes

CI/CD tooling

Model monitoring

Job description

SoftRevery is seeking an ML Engineer to take models from notebook to production and keep them healthy at scale. You will build training and evaluation pipelines, deploy, monitor, and retrain models with a modern MLOps stack. Work with data and business metrics to ship models that measurably move KPIs.

Design experiments, optimize latency and cost, and implement CI/CD for ML systems in the cloud. Proficiency in Python and PyTorch (or TensorFlow) is required, with cloud experience (AWS, GCP or