DevOps Engineer (GCP, MLOps, Integration Experience)

DevOps Engineer (GCP, MLOps, Integration Experience)

We are seeking a highly skilled DevOps Engineer with expertise in Google Cloud Platform (GCP), MLOps, and Terraform scripting to join our team in a hybrid role. As part of the DevOps team, you will be responsible for designing, developing, and maintaining cloud infrastructure and integration pipelines, particularly focused on Machine Learning Operations (MLOps) on the GCP platform.

Key Responsibilities:

• Cloud Infrastructure Management: Architect, implement, and maintain scalable and secure infrastructure on Google Cloud Platform (GCP) using Terraform scripts.
• MLOps Pipeline Development: Build and optimize MLOps pipelines for deploying machine learning models, ensuring seamless integration between data science teams and production environments.
• Automation and CI/CD: Develop and manage CI/CD pipelines, automation frameworks, and deployment processes for machine learning and other applications on GCP.
• Integration Support: Collaborate with cross-functional teams to integrate various tools, services, and APIs into the GCP environment, focusing on smooth data flows and model deployment.
• Monitoring and Optimization: Monitor system performance, troubleshoot issues, and implement proactive solutions to improve reliability, scalability, and efficiency.
• Collaboration: Work closely with data science, development, and operations teams to align DevOps processes with broader project goals and ensure the successful deployment of machine learning models and applications.

Qualifications:

• Experience:
  • 7+ years of experience as a DevOps Engineer with a focus on GCP and MLOps.
  • Proven experience in managing cloud infrastructure using Terraform for automation and infrastructure as code.
  • Strong background in setting up and managing MLOps pipelines on GCP for the deployment and lifecycle management of machine learning models.
• Technical Skills:
  • Google Cloud Platform (GCP): Extensive experience with GCP services, including Compute Engine, Cloud Storage, AI/Client services, Kubernetes Engine, etc.
  • MLOps: Strong expertise in deploying and automating machine learning workflows, including experience with tools like Kubeflow, Vertex AI, or similar.
  • Infrastructure as Code (IaC): Advanced experience with Terraform for infrastructure provisioning and management on GCP.
  • CI/CD Pipelines: Experience with Jenkins, GitLab CI, CircleCI, or other CI/CD tools.
  • Containerization: Proficiency with Docker, Kubernetes, and container orchestration on GCP.
• Soft Skills:
  • Strong problem-solving and troubleshooting abilities.
  • Excellent communication and teamwork skills, capable of working effectively in hybrid and cross-functional team environments.
  • Ability to manage competing priorities in a dynamic work setting.

Preferred Qualifications:

• Certifications:
  • GCP Professional DevOps Engineer or GCP Professional Cloud Architect certification is preferred.
• MLOps Tools: Familiarity with TensorFlow Extended (TFX), MLflow, or similar MLOps frameworks is a plus.

Work Arrangement:

This role is hybrid, requiring a combination of remote work and on-site presence at the Alpretta office for collaboration with the client. Candidates must be able to commute to the office as needed based on project requirements.