MLOps Engineer: Accelerate Production ML Pipelines

Evlo AI; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Evlo AI in San Francisco is seeking an MLOps Engineer who will build and operate the infrastructure to bring ML models from research to production at scale. You will own training pipelines, model registries, deployment automation, data/feature validation, and real-time monitoring.

You will collaborate with ML engineers, data scientists, platform security, and software teams to set tooling standards, improve release velocity, and optimize cost and reliability of AI workloads.

Qualifications

• 3–8 years of experience in MLOps or related production software role.
• Strong Python and SQL skills with tested services.
• Hands-on Docker, Kubernetes, CI/CD, and cloud experience (AWS/GCP/Azure).
• Experience with model lifecycle management and experiment tracking.
• Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry, Datadog).
• Bachelor's degree in CS/Engineering/Data Science or equivalent experience.
• Bonus: LLM inference, Ray, Spark, Airflow, Kubeflow, Terraform, or service mesh.

Responsibilities

• Build and operate ML training and deployment pipelines.
• Standardize model packaging and versioning with MLflow or Kubeflow.
• Deploy batch and online inference on cloud using Kubernetes and Terraform.
• Implement observability and alerting for model and platform performance.
• Ensure data validation to detect drift and skew before production.
• Collaborate with ML and software teams to improve testing and security.

Skills

Python

SQL

Automation

Data pipelines

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

Docker

Kubernetes

CI/CD tooling

Terraform

MLflow/Kubeflow

Job description

Evlo AI in San Francisco is seeking an MLOps Engineer who will build and operate the infrastructure to bring ML models from research to production at scale. You will own training pipelines, model registries, deployment automation, data/feature validation, and real-time monitoring.

You will collaborate with ML engineers, data scientists, platform security, and software teams to set tooling standards, improve release velocity, and optimize cost and reliability of AI workloads.