AI Infrastructure Solutions Engineer

DDN is expanding our Enterprise AI offerings to include the integration of industry leading technologies with DDN Infinia and DDN EXAScaler storage. These solutions will be optimized for inference and RAG workloads and require integration into the customer’s environment. Our support organization is deep on storage (Infinia, EXAScaler); we are now hiring an AI Infrastructure Solutions Engineer to deploy our complete AI solutions. This implementation will include NVIDIA AI Enterprise services (NIMs, NeMo, Triton, GPU Operator, licensing), vector databases (initially Milvus), RAG/agentic workflows, and the high‑performance storage and networking fabric that underpins them.

In this role, you will either remotely or onsite in some cases deploy the DDN AI solutions and work to customize this to the end user requirements. You will work with DDN internal teams, vendors and other partners as needed to successfully deploy these solutions.

• Serve as the primary technical point of contact for assigned strategic customers, that are deploying DDN AI solutions

• Work with Pre-sales to interpret design considerations during solution deployment

• Drive operational efficiency through automation, tooling, documentation, and repeatable deployment workflows

• Develop and deploy scripts and tools to support customer environments (DevOps-focused)

• Be prepared to develop scripting to deploy system monitoring and other metrics based tools to integrate with customer infrastructure

• Support AI/ML, data‑intensive, and HPC workloads running at scale in on‑prem, hybrid, and cloud‑adjacent environments

• Work closely with customers to optimize the their AI applications to better work with DDN technology

• 5+ years of experience in a senior technical role deploying complex, customer‑facing production systems

• Experience administering and operating Lustre or similar parallel file systems in large‑scale environments

• Experience with object storage and S3‑compatible systems

• Strong Linux systems knowledge, including performance tuning and troubleshooting

• Solid understanding of distributed storage architectures, networking fundamentals, and data movement at scale

• Proven ability to work directly with customers, communicate clearly, and build trusted technical relationships

• Ability to work effectively across cross‑functional teams including Engineering, Product Management, Support, and Field Services

• Experience with additional parallel file systems such as IBM Spectrum Scale or StorNext

• Experience developing and debugging automation using shell scripting, Python, Bash, or similar languages

• Strong understanding of networking technologies including InfiniBand, Ethernet, TCP/IP, and routing

• Knowledge of NVAIE services (e.g., NIMs, NeMo, Triton, TensorRT/TensorRT‑LLM, GPU Operator, licensing/NLS) and vector databases (e.g., Milvus)

• Familiarity with NAS and data transfer protocols (NFS, SMB/CIFS, SFTP, rsync, etc.)

• Experience with authentication and identity systems (LDAP, Active Directory, Kerberos, OAuth2/OIDC, SAML)

• Experience using network diagnostics and troubleshooting tools (tcpdump, Wireshark, LLDP, etc.)

• Exposure to AI/ML infrastructure operations, GPU‑accelerated environments, or large‑scale data pipelines

• Experience with deployment and orchestration of large scale compute systems (Kubernetes, SLURM, BCM etc)

• Experience working in globally distributed or remote teams

• Occasional physical tasks related to hardware setup may be required, with appropriate tools and support

• Work on real, production‑scale AI and HPC systems that power world‑class innovation

• Influence product direction through direct customer engagement

• Collaborate with highly skilled engineers across storage, networking, and distributed systems

• Grow your career into senior technical leadership, architecture, or product‑facing roles