Senior Software Engineer designing and operating ML infrastructure for Plaid's AI initiatives. Collaborating with product teams to accelerate AI - powered financial experiences and ensure scalable ML systems.
About the role
- 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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