Description

You own the lifecycle of machine learning models from development through production deployment.

Responsibilities

• Deploy and maintain multiple ML models in production at scale.
• Architect and implement ML pipelines using Kubeflow, TFX, Vertex Pipelines, or equivalent.
• Design and manage distributed model training jobs.
• Manage infrastructure for ML workloads using IaC tools.

Required Skills

• 7+ years of professional experience in data science and machine learning.
• 3+ years of hands-on experience with MLOps and production ML systems.
• Expert-level proficiency in Python.
• Deep expertise with Google Cloud Platform and Vertex AI ecosystem.
• Proficiency with ML frameworks including TensorFlow, PyTorch, and Jax.
• Strong experience with containerization (Docker) and Kubernetes.
• Expertise in SQL, BigQuery, and distributed data processing (Dataflow, Spark).
• Experience with distributed computing frameworks like Ray or Horovod.
• Familiarity with infrastructure as code using Terraform.

Key Skills

Education

Any Gradute