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Sr/Staff AI Platform Engineer

3 months ago

Delhi, India Synopsys Inc Full time
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 CloudDevelop capabilities to ship Cloud Native (Containerized) AI applications/AI systems to on-premises customersOrchestrate 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 QualificationsBS/MS/PhD in Computer Science/Software Engineering or an equivalent degree12+ years of total experience building systems software, enterprise software applications, and microservicesExpertise and/or experience in following programming languages : Go and PythonExperience building highly scalable REST APIExperience 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 APIsStrong systems knowledge in Linux Kernel, CGroups, namespaces, and DockerExperience with at least one cloud provider (AWS/GCP/Azure)Ability to solve complex problems using efficient algorithmsExperience 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 federationPrior experience with AI/ML workflows and tools (PyTorch, ML Flow, AirFlow, …)Experience prototyping, experimenting, and testing with large datasets, and analytic data flows in productionStrong fundamentals in Statistics, Machine Learning, and/or Deep Learning