MLOps Engineer leading large-scale model deployments and managing CI/CD pipelines in GCP ecosystem. Focus on operational excellence and implementing observability frameworks for AI systems.
Responsibilities
Architect and manage the end-to-end deployment of machine learning models across production environments, ensuring scalability and high availability.
Design, build, and maintain automated CI/CD pipelines using Tekton to streamline model development, testing, and release cycles.
Implement and manage comprehensive observability and traceability frameworks to monitor model health, data drift, and system performance in real-time.
Configure advanced monitoring solutions using Dynatrace and centralized logging systems to track latency, resource utilization, and system errors.
Develop and maintain MLOps infrastructure exclusively within the GCP ecosystem, utilizing Vertex AI, Google Kubernetes Engine (GKE), and BigQuery.
Automate model retraining, validation, and deployment workflows to ensure models remain accurate and performant in production.
Partner with data scientists and software engineers to transition models from research/prototypes to robust, enterprise-grade production assets.
Requirements
3+ years of professional experience in MLOps, DevOps, or Software Engineering, with a specific focus on the industrialization of machine learning models.
Bachelor’s or Master’s degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, or Mathematics).
Proven track record of building and maintaining complex, automated pipelines using Tekton or similar orchestration tools.
Demonstrated experience implementing enterprise-grade monitoring, logging, and distributed tracing in a professional environment.
Deep understanding of the GCP stack, particularly services related to model hosting, orchestration, and data management.
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