Sr/Staff AI Platform Engineer
1 month ago
Synopsys’ Generative AI Center of Excellence defines the technology strategy to advance applications of Generative AI across the company. The Gen AI COE pioneers the core technologies – platforms, processes, data, and foundation models – to enable generative AI solutions, and partners with business groups and corporate functions to advance AI-focused roadmaps.
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 Cloud 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
- 12+ years of total experience building systems software, enterprise software applications, and microservices
- Expertise and/or experience in following programming languages : Go and Python
- Experience building highly scalable REST API
- Experience with event driven software architecture and message brokers (NATS / Kafka)
- Design complex distributed systems (High-level and low-level systems design)
- Knowing CAP theorem in depth and applying it in building real-world distributed systems.
- In-Depth Kubernetes knowledge: Be able to deploy Kubernetes on-prem,working experience with managed Kubernetes services (AKS/EKS/GKE) and Kubernetes APIs
- 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:
- Experience with service meshes (Istio)
- Experience with Kubernetes cluster federation
- 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
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