What You’ll Be Doing:

• Building an AI Platform for Synopsys to orchestrate enterprise-wide data pipelines, ML training, and inferencing servers.
• Developing an "AI App Store" ecosystem to enable R&D teams to host Gen AI applications in the cloud.
• Creating capabilities to ship cloud-native (containerized) AI applications/AI systems to on-premises customers.
• Orchestrating GPU scheduling from within the Kubernetes ecosystem (e.g., Nvidia GPU Operator, MIG, etc.).
• Designing reliable and cost-effective hybrid cloud architecture using cutting-edge technologies (e.g., Kubernetes Cluster Federation, Azure Arc, etc.).
• Collaborating with cross-functional teams to experiment, train models, and build Gen AI & ML products.

The Impact You Will Have:

• Driving the development of advanced AI platforms that empower Synopsys' R&D teams.
• Enabling the creation of innovative Gen AI applications that push the boundaries of technology.
• Ensuring the efficient orchestration of data pipelines and ML training, leading to faster and more accurate AI models.
• Contributing to the development of scalable and reliable AI systems that can be deployed across various environments.
• Enhancing Synopsys' capabilities in cloud-native and hybrid cloud architecture, improving flexibility and cost-effectiveness.
• Fostering a culture of innovation and collaboration within the AI and ML engineering teams.

What You’ll Need:

• BS/MS/PhD in Computer Science/Software Engineering or an equivalent degree.
• 8+ years of experience in building systems software, enterprise software applications, and microservices.
• Expertise in programming languages such as Go and Python.
• Experience in building highly scalable REST APIs and event-driven software architecture.
• In-depth knowledge of Kubernetes, including deployment on-premises and working with managed services (AKS/EKS/GKE).
• Strong systems knowledge in Linux Kernel, CGroups, namespaces, and Docker.
• Experience with at least one cloud provider (AWS/GCP/Azure).
• Proficiency in using RDBMS (PostgreSQL preferred) for storing and queuing large datasets.