PCAI CoE Engineer

6 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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