Job Description

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.

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