DevOps Engineer, Data & AI Platform at SimplePractice

Job description

About Us At SimplePractice, we are improving access to quality care by equipping health and wellness clinicians with all the tools they need to thrive in private practice. More than 250,000 providers trust SimplePractice to build their business through our industry-leading software with powerful tools that simplify every part of practice management. From admin work to clinical care, our suite of innovative solutions work together to reduce administrative burden—empowering solo and small group practitioners to thrive alongside their clients. Award-winning and people-first, SimplePractice is shaping the future of health tech. In 2026, we were recognized by BuiltIn as a Best Remote Company to Work For, Software Guru's Best Places to Code, Becker Healthcare Telehealth Companies to Know, and we won a MedTech Breakthrough Award for Best Practice Management Solution Provider. The Role 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 Desired Skills & Experience 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 Preferred Qualifications 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 Benefits We offer a competitive benefits program including: Medical, dental, vision, life & disability insurance 401(k) plan with company match Flexible Time Off (FTO), wellbeing days, paid holidays, and summer Fridays Mental health resources Paid parental leave & Backup Care Tuition reimbursement Employee Resource Groups (ERGs) California Job Applicant Privacy Notice Thank you for your interest in opportunities at SimplePractice LLC (“SimplePractice” or “us” or “we” or “our”). Please note that when you submit your resume or application materials to us for employment purposes, you are subject to the SimplePractice California Job Applicant Privacy Notice . For more information about our privacy practices, please contact us at privacy@simplepractice.com . Notice to Candidates SimplePractice has been made aware of fraudulent job postings and unaffiliated third parties posing as our recruiting team. We do not have any affiliation or connection to these situations and only post open roles on our official Careers page ( simplepractice.com/careers ) and reputable job boards like our official LinkedIn or Indeed pages. All official SimplePractice recruitment emails will be sent from the domains @simplepractice.com, no-reply@greenhouse.io or no‑reply@linkedin.com email addresses. Examples of fraudulent domains include careers-simplepractice.com, simplepractices.com, and simplepractice.careers. Please note that SimplePractice will never ask candidates or new hires for money or payment of any kind at any stage of the recruitment or onboarding process. Base salary is one component of total compensation. Employees may also be eligible for an annual bonus or commission. Some roles may also be eligible for overtime pay. The amount below represents the expected annual base compensation range for this job requisition. Ultimately, in determining your pay, we’ll consider many factors including, but not limited to, skills, experience, qualifications, geographic location, and other job-related factors. Base Compensation Range $144,300 — $180,350 USD

Requirements

Must-haves

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
Desired Skills & Experience
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.
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

Nice-to-haves

Strong cloud experience (AWS preferred)
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlci/cddockerkubernetesmachine learningawspythonllm

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave

Wellness