Description

You will design, implement, and maintain infrastructure and processes on Google Cloud Platform (GCP) to enable seamless machine learning model development, deployment, and monitoring at scale.

Responsibilities

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

Required Skills

• 10+ years of experience in relevant engineering roles.
• Proficiency in Python.
• Expertise in GCP services including Vertex AI, Google Kubernetes Engine (GKE), Cloud Run, Cloud Composer, BigQuery, Cloud Storage, and Cloud Composer.
• Experience with infrastructure-as-code using Terraform.
• Familiarity with containerization (Docker, GKE) and CI/CD pipelines using GitLab and Bitbucket.
• Knowledge of ML frameworks such as TensorFlow, PyTorch, and 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.

Key Skills

Education

Any Graduate