AI Engineer

About the Role

• Provide support to a portfolio of technical solutions within a delivery channel, focusing on HPC, AI, and ML systems and software tools.
• Work with application, data, and infrastructure teams to produce optimal, high-level, conceptual designs for projects.
• Support enterprise-level solutions that integrate across applications, systems, and platforms.
• Manage changes in process, policy, and standards as they relate to architecture and design principles.
• Research and maintain knowledge in emerging technologies and solutions to solve business problems.
• Serve as a technical expert and critical resource across multiple disciplines.

Key Responsibilities

• Collaborate with internal stakeholders to understand future NVIDIA deployments to support project exigencies and improve DGX POD efficiency in a Kubernetes-based platform.
• Review architecture of applications and support technical design sessions with architects and developers, including the creation of class models, sequence diagrams, component models, and design specifications.
• Create project and application architecture deliverables consistent with architecture principles, standards, methodologies, and best practices.
• Research and maintain knowledge in emerging technologies and their potential applications to the business.
• Design and develop new tools to support Software Development Lifecycle (SDLC) processes.
• Serve as a liaison with the engineering team regarding required features, critical bugs, and testing of new functionality.
• Communicate the implications of architectural decisions, issues, and plans to business and IT Leadership.
• Provide input to the development of project initiation documents, including objectives, scope, approach, and deliverables, when needed.
• Partner with ITS business representatives and business leaders to understand business drivers and critical needs.
• Ensure alignment between business strategies and application technology roadmaps while advising and consulting leadership on costs, benefits, and implementation requirements.
• Support team initiatives across functions with application triage, performance engineering, and testing activities.
• Assist in the troubleshooting and triage of complex application issues.
• Provide support/guidance to development teams throughout the analysis, design, development, and testing processes.
• Resolve complex technical issues as needed to support solution development.

Requirements & Tech Stack

• Strong experience supporting Linux, OS installation and automation (PXE, kickstart, ansible), networking, and storage.
• Strong experience supporting TCP/IP networking fundamentals, ports, IP subnets, DNS, and routes.
• Expert programming/scripting skills in Linux Shell/CLI, Bash, Python, and Go.
• Strong understanding of CI/CD processes and deployment tools, including ArgoCD, Kubernetes, Helm, and Docker.
• Experience with resource management systems and job scheduling, including running and debugging parallel programs.
• Strong experience using GIT and other version control systems.
• Experience supporting large-scale data management systems serving hundreds of users/data scientists.
• Experience with provisioning and configuration management tools: Puppet, Ansible, Chef, Terraform.
• Excellent critical thinking, verbal communication, and problem-solving skills.

Qualifications

• Bachelor’s in Computer Science (CS), Computer Engineering (CSEE), or a related STEM field and/or equivalent professional experience.
• BS/MS in Computer Science (CS), Computer Engineering (CSEE), Electrical Engineering (EE), or a related/relevant STEM degree with three or more years of experience supporting HPC and AI-focused technologies (preferred).
• Familiarity with Nvidia GPU's on Linux, HPC (High Performance Computing), Infiniband, MPI, RDMA technologies (preferred).
• Experience supporting AI Data Science Projects and software tools in an HPC environment (preferred).
• Experience supporting modern deep learning software architectures and frameworks including TensorFlow, Pytorch or other frameworks (preferred).
• Familiarity with supporting different cloud providers (preferred).
• Strong expertise with Agile Methodology and supporting tools (preferred).
• Ability to effectively communicate and engage with AI engineering and data science teams (preferred).