Distinguished Architect, AI

Datadog Datadog · Enterprise · New York, NY +1 · Product Solutions Architecture

Distinguished Architect for Datadog's Product Solutions Architecture team, focusing on serving AI/LLM companies. This role acts as a technical multiplier, bridging customer infrastructure needs with Datadog's roadmap, particularly for training and deploying foundational models at scale. Responsibilities include thought leadership, strategic advisory, architecture reviews, identifying technology shifts, and collaborating with internal teams. Requires deep technical expertise in distributed systems, AI/LLM ecosystem, and observability, with strong stakeholder management and presentation skills.

What you'd actually do

  1. Demonstrate thought leadership in the AI/LLM space. Influence key decision makers and stakeholders by connecting technical capabilities to organizational and business impact.
  2. Strategically partner with highly technical Founders, Heads of Infrastructure, and Research Lead peers. Guide them on best practices and emerging industry trends in the AI/LLM space. Lead high-level technical and architectural conversations around AI adoption.
  3. Lead deep-dive architecture reviews and design engagements with customer teams and their leaders to share industry trends, best practices, and demonstrate how Datadog can support high-throughput hyper scale AI workloads.
  4. Identify emerging AI-native technology shifts and feed them directly back to Datadog Product Management. Co-create custom observability integrations and solutions alongside Product SAs to keep Datadog at the absolute forefront of the AI stack.
  5. Collaborate with Product Solutions Architecture (PSA), Sales, Sales Engineering and Marketing in providing high-quality technical resources to a broad audience of practitioners and economic buyers.

Skills

Required

  • 10+ years of experience with at-scale distributed systems architecture, high-performance computing, or large-scale infrastructure
  • Deep familiarity with the AI/LLM ecosystem
  • Deep familiarity with accelerator hardware (GPUs/TPUs)
  • Deep familiarity with modern orchestration frameworks
  • Strong understanding of best practices and real world challenges AI/LLM Ops
  • Strong understanding of LLM Observability (LLMO)
  • Excellent customer-facing presentation skills for large audiences
  • Excellent verbal and written communication skills
  • Ability to link product functionality to business objectives, value realization and ROI

Nice to have

  • Master’s degree

What the JD emphasized

  • deeply technical
  • AI/LLM space
  • AI adoption
  • high-throughput hyper scale AI workloads
  • AI-native technology shifts
  • AI stack
  • AI/LLM Ops
  • LLM Observability (LLMO)

Other signals

  • customer-focused
  • deeply technical
  • technical multiplier
  • AI labs
  • AI-native companies
  • bleeding-edge infrastructure
  • observability challenges
  • training and deploying foundational models at scale
  • AI/LLM space
  • AI adoption
  • high-throughput hyper scale AI workloads
  • AI-native technology shifts
  • AI stack
  • AI/LLM Ops
  • LLM Observability (LLMO)