Senior Machine Learning Engineer – Cloud Observability - Visa AI as Services - job 1 of 20

Ready to make a global impact by industrializing AI?

Visa AI as a Service (AIaS) operationalizes the delivery of AI and decision intelligence to ensure their ongoing business values. Built with composable AI capabilities, privacy-enhancing computation, and cloud native platforms, AIaS powers and automates industrialization of data, models, and applications for predictive and generative AI. Combined with strong governance, AIaS optimizes the performance, scalability, interpretability and reliability of AI models and services. If you want to be in the exciting payment and AI space, learn fast, and make big impacts, Visa AI as a Service is an ideal place for you!

This role is for a Sr. ML Engineer – Cloud Observability. We are seeking for a talented professional with a solid background in public cloud and AI/ML production systems. This role offers ample opportunities for learning and growth, and the chance to be part of delivering the next big thing for our AI as Services team.

Key Responsibilities:

• Implement and Maintain Cloud Observability Solutions: Build and maintain monitoring, logging and tracing systems (E.g. Prometheus, Grafana, Druid, ELK Stack) for cloud-native AI services on AWS/Azure/GCP. Partner with data engineers and data scientists to embed observability into ML workflows and ensure real-time insights.

• Collaborate on AI Model Monitoring: Work closely with data scientists and product owners to design and implement observability solutions for monitoring AI/ML model performance (e.g. accuracy, latency, data drift) in production. Develop dashboards and alerts to detect anomalies, model degradation, or bias, ensuring alignment with business SLAs.

• Automate Devops Practices:  Develop tools for automated deployment, alerting and incident response using CI/CD pipelines like Jenkins and Github flows and infrastructure as code like Terraform.

• Document & Reporting: Create and maintain clear documentation for observability processes and best practices. Generate reports to track system health and performance trends for business and technology stakeholders.

• Incident Response: Assist in diagnosing and troubleshooting issues by analyzing metrics, logs and performance data and collaborate with cross functional teams to improve system level observability from the learning.

• Stay Ahead of Trends: Explore emerging cloud and observability technologies to drive innovation.

If you are passionate about observability, cloud technology, AI, and machine learning, and are excited about making a significant impact, we would love to hear from you.

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.