Sr. Manager/AI Network Solutions
Job Description
- Key Responsibilities:
- Engage with clients to understand their AI-related challenges and opportunities, particularly in the context of connectivity requirements.
- Develop and present tailored AI connectivity infrastructure proposals that align with customer use cases, focusing on how AI workloads (e.g., GPU, LLM models) can benefit from optimized connectivity solutions.
- Work alongside the sales team, acting as a subject matter expert on AI-optimized connectivity during presales activities, helping customers understand the value of our infrastructure.
- Collaborate with data center providers, GPU partners, and internal teams (IT/Technology, engineering) to integrate and optimize AI connectivity infrastructure components.
- Analyze AI use cases and provide recommendations on the appropriate connectivity sizing for each, ensuring that network performance, bandwidth, and latency align with customer needs.
- Support the integration of AI-specific needs into the overall network strategy, ensuring the infrastructure is robust enough to handle advanced AI workloads.
- Monitor advancements in AI infrastructure technologies and integrate them into the design and implementation processes
- Work with product management to define and refine AI connectivity infrastructure offerings, ensuring alignment with customer expectations and market trends.
- Support product lifecycle management by providing insights on customer feedback, market shifts, and technical innovations that could impact the AI connectivity offering.
- Ensure alignment with the strategic goals set by the AI Network Solutions Senior Team Lead
Requirements & Qualifications:
- Bachelor’s degree in computer science, Engineering, or a related field
- Minimum of 12 years of experience in network architecture, with a focus on AI technologies
- Proven experience in designing and implementing scalable AI networks
- Ability to communicate technical concepts clearly and confidently to both business and technical audiences, without needing deep technical expertise.
- Familiarity with the connectivity needs of AI workloads such as GPU clusters, LLM models, and real-time AI analytics.
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