Production ML Engineer: Scalable Pipelines & Uptime

Evlo AI; Miami (FL); On-site; USD 110,000 - 170,000; Full time

Job summary

Evlo AI is seeking a seasoned MLOps Engineer to own the infrastructure, deployment pipelines, and reliability of production ML systems. You will scale model training, serve high-throughput inference, and implement robust monitoring across cloud environments.

Collaborate with ML engineers and data scientists to bridge research and scalable systems, focusing on latency, cost-efficiency, and governance while advancing automated CI/CD, feature stores, and secure, reproducible workflows.

Qualifications

• 3–6 years of experience in MLOps, DevOps, or ML engineering focused on production infrastructure.
• Strong proficiency in Python, Docker, and Kubernetes.
• Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) and IaC tools (Terraform).
• Deep understanding of CI/CD, monitoring stacks, and distributed data processing (Spark, Ray).
• Degree in Computer Science or Software Engineering.

Responsibilities

• Architect and maintain scalable MLOps pipelines using Docker, Kubernetes, and Terraform to automate model training, testing, and deployment.
• Design and optimize high-throughput model serving infrastructure on AWS or GCP with low latency and high availability.
• Implement monitoring and observability for deployed models to detect drift and performance degradation.
• Build automated feature stores and data pipelines for consistency across training and inference.
• Establish CI/CD pipelines for ML code, weights, and prompt artifacts.
• Collaborate with security and engineering teams to enforce governance and reproducibility standards.

Skills

MLOps

Python

Docker

Kubernetes

Terraform

AWS SageMaker

GCP Vertex AI

Azure ML

CI/CD

Monitoring (Prometheus, Grafana, Datad

Spark, Ray

Education

B.S. in CS/Software Engineering

Tools

Prometheus

Grafana

Datadog

Terraform

Job description

Evlo AI is seeking a seasoned MLOps Engineer to own the infrastructure, deployment pipelines, and reliability of production ML systems. You will scale model training, serve high-throughput inference, and implement robust monitoring across cloud environments.

Collaborate with ML engineers and data scientists to bridge research and scalable systems, focusing on latency, cost-efficiency, and governance while advancing automated CI/CD, feature stores, and secure, reproducible workflows.