DevOps Engineer, Data & AI Platform

SimplePractice

Posted 23d ago

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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

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

Skills