PCAI CoE Engineer

5 days ago

bangalore, India Taggd Full time
Key responsibilities of a PCAI CoE Engineer may include.
- Resolution of complex problems in enterprise and mission-critical customers of PCAI
- Assess and appreciate technical issues and customers' business impact, and manage technical communication with customers and other stakeholders.
- Providing leadership in complex technical problem management, working closely with end customers and HPE remote and field support staff
- Identifying and resolving customer issues, particularly with NVIDIA GPUs and related infrastructure components critical to AI processing.
- Troubleshooting and resolving issues with AI workflows leveraging CUDA, cuDNN, TensorRT, and NVIDIA Deep Learning frameworks.
- Provide technical expertise in deep learning model deployment using NVIDIA GPUs in cloud and on-premise environments.
- Work with stakeholders to identify opportunities for leveraging AI technologies to drive business solutions.
- Troubleshoot and resolve performance bottlenecks in AI workflows involving NVIDIA GPU acceleration.
- Technical support of support of Kubernetes (K8s) environments, and well-versed in AI tooling such as Apache Airflow for data orchestration and MLFlow for machine learning lifecycle management.
- Troubleshoot and optimize AI applications and infrastructure for maximum efficiency and minimal downtime.
- Fault isolation, Problem reproduction, interacting with the engineering teams, QA, and development engineers, Escalation and Elevation management
- Development of knowledge content and runbooks
Knowledge & Skills Required
AI/ML Skills
Good understanding of AI/ML and Analytics applications such as
- Kubeflow & MLflow, MLOPS – Tool (any Tools)
- Apache Sparck and Superset
- Ray, Feast, EzPresto data source,
- Data Lake
- Mlops Frame Works
- MLDE (Determined AI) -optio
- NVIDIA AI Enterprise NIM Microservices, Models, LLM, CUDA
- NVIDIA Neural Modules (NeMo) - optional
Excellent knowledge on below platform components
- Linux operating system (RHEL 8/Rocky/Ubuntu/Centos/Suse)
- Kubernetes, container runtimes and Container networking, Creating Docker Images
- Troubleshooting K8s Cluster issue
- Ezmeral-specific Kubernetes: ezkube, ezfab etc.
- Single Sign-on and IAM
- Postgres database - option
- Helm, Istio and Spire – (Istio – Service Mesh)
- Storage and CSI, CNI, operators (File Storage/ Block Storage/Object Storage)
- Troubleshooting experience on CSI & CNI
- Container base storage access protocol
NVIDIA GPU, NVIDIA AI and related software’s
- Good Knowledge of GPU technologies, NVIDIA GPU operator, NVIDIA vGPU technology
- Strong GPU Understanding and troubleshooting skills at the HW, OS, SW and Application layers.
- Experience with NVIDIA SDKs (e.g., DeepStream, Jetson, etc.) and GPU performance tuning.
- Experience with NVIDIA Jetson for edge AI development.
- Knowledge of MLOps and experience with AI model deployment pipelines.
- Familiarity with containerization and deployment using Docker and Kubernetes on GPU-powered systems.
- Familiarity with NVIDIA’s AI software stack, including Triton Inference Server, NVIDIA Clara, and NVIDIA Isaac and scalable AI workflows leveraging CUDA, cuDNN, TensorRT, and NVIDIA Deep Learning frameworks
- Experience with cloud platforms such as AWS, Azure, or Google Cloud for NVIDIA GPU-based AI model deployment.
- performance profiling, tuning, and optimization of AI applications on NVIDIA GPUs.
OS, Networking & Virtualization
Excellent understanding of
- VMware vCenter + ESXi 7 & 8, , Switching. Layer 2 Networking, Cluster management
- HA, DRS
- Storage access protocol for VMware
- Content libraries and OVA/Template management & deployment
- Qumless OS
- VMware VMFS datastore management
- Knowledge on VMware standard vswitches, VMkernel interfaces and VDS would be a bonus
- NFS storage configuration and troubleshooting would be desired
Other skills
- Good knowledge and hands-on experience with at least two various Linux distributions like RHEL, SLES, Ubuntu, and Debian.
- Knowledge and experience with Linux System Administration, package management, scheduling, boot procedures/troubleshooting, performance optimization, and networking concepts.
- Windows AD administration (user management for EZ authentication integration)
- IPV6 + SLAAC
Common skills and qualifications
- Education: A bachelor's or master's degree in computer science, information technology, or a related field is preferred.
- Problem-Solving Skills: Excellent problem-solving skills and the ability to diagnose and resolve complex technical issues.
- Communication Skills: Effective communication skills to collaborate with other teams, including development, security, and compliance teams.
- Collaboration Skills: The ability to work effectively in a team environment and to coordinate efforts with other teams to resolve issues and implement new solutions.
- IT Service Management Experience: Familiarity with IT service management (ITSM) frameworks, such as ITIL, and experience with incident, problem, and change management processes.

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