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Strong employer brand and metro location, but senior niche ML-platform role reduces applicant competition.
Requires specialized GCP/Vertex AI and ML-platform experience, making cross-industry portability limited.
Multiple mandatory GCP, Vertex AI, ML platform, and 8+ years requirement make filters highly stringent.
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Own end-to-end ML platform engineering on Google Cloud Platform (GCP) for the RAPTOR platform, focusing on Vertex AI and Kubeflow-based scalable pipelines and real-time inference.
Manage ML lifecycle operations including training, deployment, monitoring, retraining, incident response, and production troubleshooting for AI/ML systems.
Drive platform modernization, automation (Terraform, Ansible), CI/CD pipelines, container orchestration (Docker, Kubernetes), and developer enablement for ML platform support.
8+ years of relevant experience in data science, ML engineering, or platform engineering.
Strong hands-on experience with Google Cloud Platform (GCP), especially Vertex AI, Kubeflow, Dataflow, BigQuery, Pub/Sub, Cloud Composer, and Cloud Run.
Proficiency in Python, SQL, Bash scripting, REST API development, containerization (Docker), and orchestration (Kubernetes).
Work Experience Required: 8+ years. Notice period: Not explicitly mentioned in the JD.
Expertise in end-to-end ML platform operation on GCP with ability to manage real-time streaming ML systems and automated pipelines.
Experience working in complex production environments requiring incident response, monitoring, and high availability of ML services.
Capable of integrating infrastructure automation, CI/CD, and developer support to enhance AI/ML platform scalability and reliability.