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# DevOps Engineer, Data & AI Platform

- **Company:** SimplePractice, LLC.
- **Location:** Santa Monica, CA, United States
- **Experience:** Experienced
- **Salary:** $144,300.0 - $180,350.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Bash Shell, Computer Programming, Databases, Continuous Integration, Data Systems, DevOps, Python (Programming Language), Machine Learning, Data Streaming, Management of Software Versions, Data Logging, Scripting, Large Language Models, Grafana, Apache Spark, Usage Tracking, Containerization, AI Platforms, Git Flow, Kubernetes, Infrastructure Automation Frameworks, Apache Kafka, Data Management, Machine Learning Operations, Terraform, Data Pipelines, Automation Anywhere, Docker
- **Published:** August 26, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88150506/1

## About the Role

* 3+ years of experience in DevOps, SRE, or infrastructure engineering
 * End-to-End MLOps/LLMOps Expertise: Experience deploying and maintaining ML/AI workflows. Familiarity with the unique nature of promoting AI assets (models, datasets, and code) through the lifecycle.
 * Strong cloud experience (AWS preferred)
 * Proficiency with Terraform (or similar IaC tools)
 * Experience with Docker and Kubernetes
 * Familiarity with CI/CD and Git-based workflows
 * Experience supporting data platforms (e.g., Airflow, Kafka, Spark, or similar)
 * Programming/scripting (Python, Bash, or similar)
 * Experience with observability tools and practices, * Experience with MLOps tooling (e.g., MLflow, SageMaker, Kubeflow)
 * Familiarity with LLM-based systems and AI observability(token usage tracking, prompt versioning) and evaluation loops
 * Experience with real-time or high-throughput data systems
 * Exposure to security and compliance requirements (e.g., SOC 2, HIPAA)
 * Experience with specific MLOps tooling (Outerbounds, SageMaker, Metaflow) and vector database

## Description

We are hiring a DevOps Engineer to support and scale our Data and AI platform in production. This 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

 * 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


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