Job description 

We are looking for an experienced, passionate, and self-driven individual who possesses both a broad technical strategy and the ability to tackle architectural and modernization challenges. As an Ideal candidate will help build enterprise Machine Learning platform. They will work with a team of enthusiastic and dynamic ML engineers and Data scientists in building a platform to help Synopsys R&D teams to experiment, train models and build Gen AI & ML products. 

You will be responsible for:

• Building AI Platform for Synopsys to orchestrate enterprise-wide Data pipelines, ML training, and inferencing servers.
• Develop "AI App Store" eco system to enable R&D teams to host Gen AI applications in Cloud
• Develop capabilities to ship Clou Native (Containerized) AI applications/AI systems to on-premises customers
• Orchestrate GPU Scheduling from within Kubernetes eco-system (e.g. Nvidia GPU Operator, MIG, and so on)
• Create reliable and cost-effective Hybrid cloud architecture using cutting edge technologies (E.g. Kubernetes Cluster Federation, Azure Arc and so on)

Required Qualifications

• BS/MS/PhD in Computer Science/Software Engineering or an equivalent degree
• 10+ years of total experience building systems software, enterprise software applications, ML applications and microservices
• Expertise and/or experience in following programming languages : Python, and Go
• Design complex distributed systems (High-level and low-level systems design)
• In-Depth Kubernetes knowledge: Be able to deploy Kubernetes on-prem, and working experience with managed Kubernetes services (AKS/EKS/GKE)
• Strong systems knowledge in Linux Kernel, CGroups, namespaces, and Docker
• Experience with at least one cloud provider (AWS/GCP/Azure)
• Ability to solve complex problems using efficient algorithms
• Experience with using RDBMS (PostgreSQL preferred) for storing and queuing large sets of data

Nice to have:

• Prior experience with AI/ML workflows and tools (PyTorch, ML Flow, AirFlow, …)
• Experience prototyping, experimenting, and testing with large datasets, and analytic data flows in production
• Strong fundamentals in Statistics, Machine Learning, and/or Deep Learning