Job Summary
The GCP DevOps & Data Engineer / Data Scientist is responsible for developing and maintaining cloud-native applications, AI/ML solutions, and scalable data pipelines using Python, Google Cloud Platform (GCP), Databricks, and Google's Gemini API. The role involves building backend services, designing secure and efficient APIs, implementing CI/CD pipelines using GitOps methodologies, managing build artifacts, and automating infrastructure provisioning. The engineer will work with GCP data services, event-driven integrations, and serverless architectures while collaborating with Product, Data Engineering, and Infrastructure teams to deliver scalable AI solutions. The position requires hands-on experience with cloud engineering, data processing, DevOps practices, and GCP security standards.
Key Responsibilities
• Develop AI/ML applications leveraging Google's Gemini API for natural language understanding and generation.
• Build and enhance cloud-native applications using Python, Databricks, and Google Cloud Platform.
• Design, develop, and maintain scalable backend services using Cloud Run and serverless architectures.
• Develop and optimize APIs using Google Cloud API Gateway while following security and performance best practices.
• Build and maintain AI/ML data pipelines using Databricks.
• Develop and maintain CI/CD pipelines using GitOps methodologies.
• Manage build artifacts and repositories using JFrog Artifactory and Azure DevOps.
• Work with GCP data services, including BigQuery, AlloyDB, Cloud Storage, and Spanner.
• Develop event-driven integrations using Pub/Sub and Kafka.
• Implement Infrastructure as Code (IaC) using Terraform.
• Collaborate with Product, Data Engineering, and Infrastructure teams to deliver scalable AI solutions.
• Apply GCP security, Identity and Access Management (IAM), encryption, and cloud best practices throughout the development lifecycle.
Required Qualifications
• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3-5 years of experience in backend development, AI/ML engineering, DevOps, or cloud engineering.
• Hands-on experience with Google Cloud Platform services, including Cloud Run, Google Kubernetes Engine (GKE), BigQuery, AlloyDB, Cloud Storage, and serverless technologies.
• Experience developing applications using Python.
• Working knowledge of Databricks for AI/ML and data processing workloads.
• Experience with REST API development and Google Cloud API Gateway.
• Experience with CI/CD, GitOps, Azure DevOps, and JFrog Artifactory.
• Experience implementing Infrastructure as Code using Terraform.
• Experience with Pub/Sub or Kafka for event-driven integrations.
• Understanding of GCP IAM, encryption, and cloud security best practices.
• Strong analytical, troubleshooting, and problem-solving skills.
• Good communication and collaboration skills.
