Data Engineer II

Data Engineer II role focused on migrating data pipelines and AI/ML workloads to a federated AI platform, developing distributed AI workflows, and providing technical support to data science teams. Requires experience in cloud environments, AI/ML solutions, and Python development.

What you'd actually do

  1. Work directly with data scientists and AI model developers to migrate data pipelines and training/inference workloads onto a federated AI platform, ensuring scalability and performance.
  2. Research, design, develop, and test distributed AI workflows, including data preprocessing, model training, and inference across AWS, GCP, and Azure environments.
  3. Provide hands-on technical support to data science teams, including troubleshooting VM, OS-level, and cloud infrastructure issues impacting model execution.
  4. Develop and maintain expertise in federated learning frameworks, including NVIDIA FLARE, to enable secure and efficient distributed model development.

Skills

Required

  • Python
  • AWS, GCP, or Azure
  • Data pipelines
  • AI/ML workloads
  • Cloud networking fundamentals
  • Agile environments

Nice to have

  • Refactoring and optimizing code
  • Identity and access management concepts
  • Modular cloud platforms
  • Orchestration tools
  • Model control processes

What the JD emphasized

  • 2+ years of experience working in agile environments with hands-on involvement in testing and validating data, ML, or distributed workflows
  • 2+ years of experience implementing solutions in cloud environments (AWS, GCP, or Azure), including deploying data pipelines or AI/ML workloads
  • 2+ years of experience supporting or contributing to AI/ML or GenAI solutions, with exposure to model architecture, training, or inference workflows
  • 2+ years of experience developing in Python for data engineering, automation, or machine learning use cases

Other signals

  • migrating data pipelines and training/inference workloads onto a federated AI platform
  • developing and testing distributed AI workflows
  • supporting data science teams with infrastructure issues impacting model execution
  • federated learning frameworks