AI Data Platform DevOps Engineer

SimplePractice, LLC; United States; Remote; USD 144,000 - 180,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) plan with company match

Flexible Time Off

Job summary

SimplePractice is hiring a DevOps Engineer to support and scale our Data and AI platform in production. The role focuses on building reliable infrastructure for data pipelines and ML systems, standardizing deployment patterns, and ensuring performance, observability, and cost efficiency across compute-intensive workloads.

Responsibilities include building CI/CD for model lifecycle, managing Terraform-based infrastructure, and collaborating with ML teams to productionize models while enforcing

Qualifications

• 3+ years in DevOps, SRE, or infrastructure engineering.
• End-to-end MLOps/LLMOps expertise for ML workflows.
• Strong cloud experience (AWS preferred).
• Proficiency with Terraform (IaC).
• Experience with Docker and Kubernetes.
• Familiarity with CI/CD and Git workflows.
• Experience with data platforms (Airflow, Kafka, Spark).
• Programming in Python or Bash.
• Observability tools and practices experience.

Responsibilities

• Build and operate infrastructure for data pipelines and AI/ML workloads.
• Develop and maintain CI/CD for application and model lifecycle (build, train, deploy).
• Manage Infrastructure as Code (Terraform) across environments.
• Support containerized workloads and orchestration (Docker, Kubernetes).
• Partner with Machine Learning teams and engineering to productionize models.
• Implement monitoring, logging, and tracing for data flow and model performance.
• Improve reliability, scalability, and cost efficiency of data systems.
• Enforce security and access controls for data and infrastructure.
• Reduce operational overhead through automation and tooling.

Skills

DevOps/SRE

MLOps

AWS

Terraform

Docker

Kubernetes

CI/CD

Python

Bash

Observability

Airflow

Kafka

Spark

Job description

SimplePractice is hiring a DevOps Engineer to support and scale our Data and AI platform in production. The role focuses on building reliable infrastructure for data pipelines and ML systems, standardizing deployment patterns, and ensuring performance, observability, and cost efficiency across compute-intensive workloads.

Responsibilities include building CI/CD for model lifecycle, managing Terraform-based infrastructure, and collaborating with ML teams to productionize models while enforcing