Data Engineer

• MLOps Pipeline Ownership: Design, implement, and manage end-to-end MLOps pipelines for the continuous training, deployment, monitoring, and versioning of high-impact ML models.
• Data Pipeline Design & Orchestration: Design, build, and maintain robust data ingestion and transformation pipelines.
• Kubernetes and Cloud Architecture: Architect, deploy, and maintain highly scalable, fault-tolerant infrastructure using Kubernetes across Google Cloud Platform (GCP).
• DevOps and Automation: Drive DevOps culture and best practices, focusing on Infrastructure as Code (IaC) using Terraform or similar tools.
• CI/CD Implementation: Configure and manage automated deployment and testing pipelines using GitHub Actions and other integrated tools.
• Core Development: Write clean, efficient, and well-tested code in Python for automation scripts and infrastructure tooling.
• API and Service Deployment: Design, develop, and deploy robust, high-performance Python APIs to serve machine learning predictions.
• Monitoring and Observability: Implement comprehensive monitoring, logging, and alerting solutions.
• Security and Compliance: Ensure platform integrity by implementing security best practices.
• Collaboration and Mentorship: Work closely with data scientists, software engineers, and product managers; provide technical guidance and mentorship.

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Junior Data Engineer

Junior Data Engineer

Staff Data Engineer