Description

You will manage and automate machine learning infrastructure and production pipelines. Responsibilities include automating ML pipelines using tools such as MLflow, Kubeflow, or Airflow. You will manage ML workloads on cloud platforms including AWS and Azure. This role requires implementing CI/CD principles and containerization strategies. You will deploy monitoring and alerting systems for ML models using Prometheus or Grafana. Travel to Pennsylvania, NYC, or Toronto is required for initial project onboarding and monthly follow-ups.

Responsibilities

• Automate ML pipelines using MLflow, Kubeflow, or Airflow.
• Manage ML workloads on AWS and Azure.
• Implement CI/CD principles and containerization strategies.
• Deploy monitoring and alerting systems for ML models using Prometheus or Grafana.
• Travel to Pennsylvania, NYC, or Toronto for onboarding and follow-ups.

Required Skills

• 8+ years of experience in MLOps, DevOps, data engineering, or ML infrastructure.
• Proficiency with AWS and Azure.
• Strong experience with SQL.
• Hands-on expertise with Docker and Kubernetes.
• Proven ability to build automated ML pipelines.
• Deep understanding of CI/CD workflows.
• Experience with monitoring tools like Prometheus and Grafana.
• Bachelor's degree in Computer Science, Data Science, Engineering, or equivalent experience.
• Must be located in the EST time zone.

Key Skills

Education

Bachelor's degree in Computer Science