✨ AI Insights & Summary
This DevOps Engineer role at SimplePractice offers a compelling opportunity to be at the forefront of scaling a cutting-edge Data & AI platform. You'll architect and maintain the robust infrastructure essential for complex data pipelines and machine learning systems, directly impacting the company's ability to deliver innovative AI-driven solutions. If you're passionate about building reliable, performant, and cost-efficient MLOps environments in a cloud-native setting, this is your chance to make a significant impact.
Role Summary
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
Required Qualifications
- 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:
- Privatized Medical, Dental & Vision Coverage
- Work From Home stipend
- Flexible Time Off (FTO), wellbeing days, paid holidays, and Summer Fridays
- Monthly Meal Reimbursement
- Holiday Bonus, 15-day Aguinaldo
- Hybrid Work Schedule & Catered Lunch
- A relocation bonus for candidates joining us from a different city
- 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.
Compensation
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
$1,124,480—$1,405,600 MXN