Description

You will build and maintain the infrastructure and pipelines required for Generative AI applications.

Responsibilities

• Design and implement MLOps pipelines for generative AI models, covering data ingestion, pre-processing, training, deployment, and monitoring.
• Automate ML tasks across the model lifecycle using GitOps, CI/CD pipelines, and containerization.
• Develop and maintain monitoring and alerting systems to ensure model reliability in production.
• Manage cloud infrastructure for ML workloads to ensure scalability and cost-efficiency.
• Collaborate with the Generative AI Full Stack Architect to optimize model performance and resource utilization.

Required Skills

• 8+ years of experience in MLOps, DevOps, data engineering, or ML infrastructure.
• Proven experience automating ML pipelines with tools such as MLflow, Kubeflow, or Airflow.
• Hands-on expertise with AWS or Azure for ML workloads.
• Strong proficiency in Python.
• Experience with containerization using Docker and Kubernetes.
• Deep understanding of CI/CD principles.
• Experience with monitoring and alerting tools like Prometheus or Grafana.
• Familiarity with Generative AI workflows.
• Bachelor's degree in Computer Science, Data Science, or a related field.

Key Skills

Education

Bachelor's degree in Computer Science, Data Science