Ddn

AI Infrastructure Solutions Engineer

Madrid, Madrid, Spain · On-site · FullTime

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About the role

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EmployerDdn
LocationMadrid, Madrid, Spain
Work styleOn-site
Employment typeFullTime
Employer posted2 months ago

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.

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Key Responsibilities

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

Required Qualifications

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

Preferred Qualifications

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

Additional Information

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

Why Join DDN

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

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