Description

You will develop, train, and validate machine learning models, managing the full lifecycle from experimentation to production inference.

This role is on-site.

Responsibilities

• Build and optimize Python-based runtimes for model training, inference, and feature engineering.
• Orchestrate ML workflows and production inference pipelines using Argo Workflows on Kubernetes.
• Leverage Vertex AI for scalable model training, hyperparameter tuning, and validation.
• Manage model artifacts in Google Cloud Storage and track metadata, versions, and lineage.
• Define evaluation metrics, support post-deployment monitoring, drift detection, and retraining strategies.

Required Skills

• 5+ years of hands-on proficiency in Python for data science and machine learning.
• Strong experience with ML frameworks such as PyTorch, scikit-learn, or CatBoost.
• Deep understanding of feature engineering, model evaluation, and validation techniques.
• Experience taking models from experimentation to production inference.
• Familiarity with Kubernetes and Kubernetes-based workflows, specifically Argo Workflows.
• Knowledge of MLOps principles and ML lifecycle management.
• Experience with Google Cloud Platform (GCP) services.

Preferred Skills

• Ability to translate complex business problems into data science solutions.
• Experience participating in on-call rotations for inference infrastructure.

Key Skills

Education

Any Gradute