MLOps Engineer: Scalable Pipelines & Model Serving

Evlo AI; Seattle (WA); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI in Seattle seeks an MLOps Engineer to design scalable ML pipelines, deploy training and inference workloads, and maintain robust serving infrastructure.

You will implement feature stores, model registries, and observability dashboards, enabling reliable, observable ML deployments at scale. Expect collaboration with ML engineers and data scientists in a fast-moving environment.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• 3–6 years of experience in MLOps, DevOps, or ML engineering with ownership of at least one model serving system in production.
• Strong Python and/or Go skills, plus fluency with Docker and Kubernetes in production settings.
• Hands-on experience with ML platforms and tooling: MLflow, Kubeflow, SageMaker, or Vertex AI pipelines.
• Experience building monitoring and alerting for ML systems - drift detection, performance regression, and serving SLOs.
• Solid infrastructure-as-code skills (Terraform or Pulumi) and cloud experience on AWS, GCP, or Azure.

Responsibilities

• Design and maintain CI/CD pipelines for ML workloads across training, evaluation, and deployment stages.
• Build and operate model serving infrastructure on Kubernetes with autoscaling and canary rollouts.
• Implement feature stores and data pipelines to ensure consistency between training and inference environments.
• Instrument model observability with drift detection, latency/throughput monitoring, and alerts via Prometheus and Grafana.
• Manage model registries and versioning to trace production models to data, code, and training runs.
• Optimize infrastructure costs across GPU/spot capacity and multi-cloud/hybrid targets.
• Automate reproducibility via containerized training environments and infrastructure-as-code.

Skills

Python

Go

CI/CD

MLOps

Observability

Education

Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

Tools

Docker

Kubernetes

MLflow

Kubeflow

SageMaker

Vertex AI

Airflow

dbt

Spark

Prometheus

Grafana

Terraform

Pulumi

Job description

Evlo AI in Seattle seeks an MLOps Engineer to design scalable ML pipelines, deploy training and inference workloads, and maintain robust serving infrastructure.

You will implement feature stores, model registries, and observability dashboards, enabling reliable, observable ML deployments at scale. Expect collaboration with ML engineers and data scientists in a fast-moving environment.