Description

You will design and maintain scalable ML data pipelines for batch and real-time processing on Google Cloud Platform.

This role is hybrid.

Responsibilities

• Implement and optimize MLOps workflows, including model deployment, monitoring, and continuous learning.
• Ensure data quality, governance, and lineage while managing large-scale connected vehicle datasets.
• Automate infrastructure and deployments using Terraform, CI/CD pipelines, and DevOps best practices.
• Monitor and troubleshoot production pipelines, improving performance, reliability, and cost efficiency.
• Collaborate with cross-functional teams to deliver data products and support AI/ML-driven initiatives.

Required Skills

• 5+ years of experience in data engineering, MLOps, or cloud-based data solutions.
• Strong expertise in Google Cloud Platform services: BigQuery, Pub/Sub, and Dataflow.
• Hands-on proficiency in Python and SQL for building scalable data and ML pipelines.
• Experience with streaming frameworks and real-time data processing.
• Proven track record with MLOps, CI/CD, and DevOps practices.
• Proficiency with Terraform, Docker, and GitHub for infrastructure as code and version control.
• Knowledge of data governance, pipeline monitoring, and production support.

Preferred Skills

• Bachelor’s or master’s degree in computer science, Data Engineering, Machine Learning, or a related field.

Key Skills

Education

Any Graduate