Description

You will design, implement, and maintain GCP infrastructure to enable scalable machine learning model development, deployment, and monitoring.

This role is on-site.

Responsibilities

• Design and implement CI/CD pipelines for deploying ML models into production using Vertex AI, Cloud Run, or Cloud Composer.
• Build and maintain scalable GCP infrastructure, including Google Kubernetes Engine (GKE) and Cloud Storage.
• Develop automated workflows for data ingestion, model training, validation, and deployment integrated with GitLab and Bitbucket.
• Implement monitoring solutions using Cloud Monitoring and Logging to track model performance, data drift, and system health.
• Manage versioning of datasets, models, and code to ensure reproducibility and traceability.

Required Skills

• 10+ years of experience in relevant engineering roles.
• Proficiency in Python.
• Expertise in GCP services: Vertex AI, GKE, Cloud Run, Cloud Composer, BigQuery, Cloud Storage.
• Experience with infrastructure-as-code using Terraform.
• Familiarity with containerization (Docker, GKE) and CI/CD pipelines (GitLab, Bitbucket).
• Knowledge of ML frameworks: TensorFlow, PyTorch, scikit-learn.
• Understanding of data engineering concepts, including ETL pipelines with BigQuery and DataProc-PySpark.
• Experience integrating GCP-native tools with open-source MLOps frameworks like MLflow or Kubeflow.

Preferred Skills

• Experience with large-scale distributed systems.
• Familiarity with advanced data engineering patterns.

Key Skills

Education

Any Graduate