DevOps Engineer, Data & AI Platform

SimplePractice is a health technology company providing practice-management tools for health and wellness clinicians. The company is seeking a DevOps Engineer to support and scale its Data and AI platform in production by building reliable infrastructure, standardizing deployment patterns, and improving performance, observability, and cost efficiency.

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

Requirements:

• 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