Job Description

Internal Talent Acquisition Leader | Google Analytics About the Role: We are seeking a highly motivated and experienced GCP Cloud Engineer with a strong background in cloud computing, MLOps , and machine learning . The ideal candidate will design and optimize scalable cloud-native solutions to support machine learning workflows, ETL pipelines, and production-ready architectures. The engineer will be responsible for leveraging Google Cloud Platform (GCP) technologies to create secure, efficient, and high-performing systems, ensuring the seamless deployment and operation of machine learning models at scale. Responsibilities: Design, develop, and optimize cloud-native solutions for machine learning and data processing on Google Cloud Platform (GCP) . Implement and manage MLOps workflows for the deployment, monitoring, and management of machine learning models at scale. Utilize GCP tools like Vertex AI , Kubeflow Pipelines , AI Platform , and others to streamline and automate machine learning workflows and ETL pipelines. Develop and maintain scalable, high-performance data architectures using technologies such as BigQuery , Dataflow , Pub/Sub , Compute Engine , and GKE . Ensure high security, availability, and performance of cloud infrastructure while managing cost efficiency and operational overhead. Work closely with cross-functional teams to gather requirements and deliver cloud solutions that align with business goals. Automate the deployment and scaling of cloud infrastructure through Cloud Functions , Terraform , or other automation tools. Troubleshoot and resolve issues related to cloud infrastructure, networking, and service failures. Keep up with the latest cloud technologies, best practices, and industry trends to ensure the team is leveraging the best available tools and methodologies. Qualifications: 3-4 years of experience in cloud computing, MLOps, and machine learning with a strong focus on building cloud-native solutions. Proven experience with Google Cloud Platform (GCP) , including hands-on expertise with technologies such as Vertex AI , Kubeflow Pipelines , AI Platform , BigQuery , Dataflow , Pub/Sub , Compute Engine , GKE , and Cloud Functions . Strong programming skills in Python and experience working with cloud infrastructure automation and deployment tools (e.g., Terraform, Ansible). Solid understanding of machine learning frameworks and workflows, with experience in automating deployment pipelines for ML models. Demonstrated ability to design scalable and secure cloud architectures to support ML models , ETL pipelines , and large-scale data processing. Experience in troubleshooting and optimizing cloud infrastructure for high availability, performance, and cost-effectiveness. Strong knowledge of cloud security best practices, data privacy, and compliance requirements. Excellent communication skills and the ability to collaborate with cross-functional teams. Preferred Qualifications: GCP Professional Cloud Architect Certification or similar GCP certifications. Experience with additional machine learning tools and platforms like TensorFlow , PyTorch , or Kubernetes . Familiarity with DevOps practices, including CI/CD pipelines and infrastructure as code (IaC). Prior experience in designing and managing production-level AI/ML systems and data pipelines. Seniority level Mid-Senior level Employment type Full-time Job function Engineering and Information Technology Industries IT Services and IT Consulting #J-18808-Ljbffr Resolve Tech Solutions

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