ML DevOps Engineer

Reuben Cooley Inc.; Chicago (IL); Sur place; USD 140 000 - 190 000; Plein temps

Résumé du poste

Reuben Cooley Inc. is seeking an ML DevOps Engineer to build and run the pipelines and infrastructure that take machine learning models from development into production.

You will work with data scientists, engineers and platform teams to ensure reliable deployment, strong monitoring and secure scaling as models evolve. Ideal candidates have 5+ years total experience with 3+ years in MLOps/DevOps for ML workloads, hands-on with cloud platforms (AWS/Azure/GCP), Docker, Kubernetes and CI/CD tools,

Qualifications

• 5+ years in IT, with 3+ years in MLOps or DevOps for ML/AI workloads.
• Build and run pipelines and infrastructure to deploy models reliably and securely.

Responsabilités

• Build and run pipelines and infrastructure for ML/AI deployment.
• Collaborate with data scientists, engineers and platform teams.
• Ensure production models are monitored, secure and scalable.

Connaissances

Cloud platforms

MLOps tools

Container orchestration

CI/CD tooling

Scripting (Python & Bash)

Monitoring & observability

ML lifecycle understanding

Outils

AWS

Azure

GCP

MLflow

Kubeflow

SageMaker

Azure ML

Databricks

Docker

Kubernetes

Jenkins

GitHub Actions

GitLab CI

Azure DevOps

Terraform

CloudFormation

Prometheus

Grafana

Datadog

CloudWatch

Description du poste

We are looking for an ML DevOps Engineer to build and run the pipelines and infrastructure that take machine learning and AI models from development into production. You'll work with data scientists, engineers and platform teams to make sure models are deployed reliably, monitored well and kept secure as they scale.

Experience

• 5+ years overall, with 3+ years in MLOps or DevOps for machine learning and AI workloads

Required Skills

• Strong hands-on experience with at least one major cloud platform: AWS, Azure or GCP.
• Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Azure ML or Databricks.
• Skilled with Docker and Kubernetes.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI or Azure DevOps.
• Infrastructure as Code with Terraform, CloudFormation or similar tools.
• Good scripting skills in Python and Bash.
• Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog or CloudWatch.
• Good understanding of the ML lifecycle and how models are served in production.

Good to Have Skills

• Experience deploying and running Generative AI or LLM-based applications, including API gateways and model serving.
• Cloud certifications such as AWS DevOps or ML Specialty, or Azure DevOps Engineer.
• Exposure to finance or other regulated industries.
• Experience in large enterprise or client-facing consulting settings.
• Design, build and maintain CI/CD pipelines for ML and AI models.
• Automate model training, testing, deployment and rollback.
• Set up and manage cloud infrastructure for model hosting and serving.
• Containerize models and services, and manage them on Kubernetes or similar platforms.
• Put monitoring in place for model performance, data drift, latency and cost.
• Manage model versioning, experiment tracking and the model registry.
• Work with data science teams to make models production-ready.
• Apply security, access control and compliance standards across ML environments.
• Troubleshoot production issues and support reliable, always-on operations.
• Document processes and promote best practices across teams.