ML Ops Engineer

Towards AI, Inc.; El Segundo (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Towards AI, Inc. is seeking an experienced ML Ops Engineer to own the infrastructure and lifecycle of production ML systems. You will build and maintain production pipelines, deployment infrastructure, and monitoring systems for predictive models in a clinical monitoring platform.

You will manage model versioning and experiment tracking while ensuring reliability of training and deployment workflows. Collaboration with cross‑functional teams is essential to support experimentation-to-production

Qualifications

• At least four years of experience in MLOps, ML engineering, DevOps, or related infrastructure roles.
• Proficiency in Python, Apache Airflow, MLflow, and AWS services is required.
• Strong debugging skills with SQL and data warehousing experience; knowledge of infrastructure-as-code and containerization.

Responsibilities

• Own the infrastructure and operational lifecycle of production ML systems for a clinical monitoring platform.
• Build and maintain production pipelines, deployment infrastructure, and monitoring systems to support predictive models.
• Manage model versioning, experiment tracking, and reliability of training and deployment workflows.
• Collaborate across technical and clinical teams to transition from experimentation to production.
• Implement operational observability and data versioning; apply infrastructure-as-code to ensure scalability and performance.

Skills

ML Ops

ML Engineering

DevOps

Data Infrastructure

Data Science

MLOps Lifecycle

Experiment Tracking

Model Versioning

Observability

Cloud

Python

SQL

Containerization

Infrastructure as Code

Tools

Airflow

MLflow

AWS

Docker

Jenkins

Terraform

Kubernetes

Apache Spark

GitHub Actions

DVC

TensorFlow

Dask

Job description

# ML Ops Engineer## Circadia HealthPublished 17 Jul 2026### Share this jobEl SegundoFull Time## Role Highlights### Languages usedPythonS3SQLTriton### Key skillsData EngineerData InfrastructureML ResearchTraining DataMachine LearningML OpsData ScienceIntegrationsCICDDistributed SystemsData ProcessingData QualityAutomated TestingData WarehousingGitHub ActionsOperationsDeploymentBackendCloudReliabilityBatchInferenceEmbeddedLoggingStorageArchitectureSecuritySOCDevopsAutomationIAMDebuggingStreamingStartupAIGoogle Analytics### Tools, Libraries and FrameworksAirflowAWSFirmWareMLFlowEC2SnowFlakeCloudWatchDockerJenkinsDVCApache SparkRuby On RailsTensorflowDask## DescriptionThe role involves owning the infrastructure and operational lifecycle of machine learning systems for a clinical monitoring platform. The engineer will build and maintain production pipelines, deployment infrastructure, and monitoring systems to support predictive models. Responsibilities include managing model versioning, experiment tracking, and ensuring the reliability of training and deployment workflows. The position requires collaborating across various technical and clinical teams to facilitate the transition from experimentation to production. Additionally, the role focuses on implementing operational observability, data versioning, and infrastructure-as-code to ensure system scalability and performance.## Required Qualifications and SkillsCandidates must possess at least four years of experience in MLOps, ML engineering, DevOps, or a related infrastructure role. Proficiency in Python, Apache Airflow, MLflow, and AWS services is required, alongside experience with containerization and infrastructure-as-code. Applicants should have a solid understanding of the machine learning lifecycle, including training, deployment, and monitoring. While no specific degree is mentioned, the role requires strong debugging skills and familiarity with SQL and data warehousing platforms.