About this role
The Product Solutions Architecture (PSA) team acts as a technical multiplier across Datadog. PSAs are domain experts who partner with Field teams on complex customer use cases across pre- and post-sales engagements and scale their impact by producing reusable collateral, including reference architectures, technical guides, and enablement assets. By feeding real-world customer insights back to Datadog Product teams, PSAs help influence product roadmaps while accelerating adoption, usage, and long-term customer success.
Datadog’s AI Agent Observability product enables organizations to monitor, troubleshoot, and optimize large-scale AI-powered applications with confidence, while meeting requirements around data privacy, compliance, and cost management. As a Product Solutions Architect, you will partner closely with Datadog customers and the AI Agent Observability product team to design architectures, implement best practices, and drive adoption of AI Agent Observability across customer environments in two ways – performance, and reliability.
At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.
What You’ll Do:
• Serve as the in-house subject matter expert for Datadog’s AI Agent Observability product
• Partner with Field teams to provide hands-on technical and architectural guidance to enterprise customers adopting AI Agent Observability
• Create high-impact technical collateral, including reference architectures, technical guides, cookbooks, and documentation to enable Field teams and the broader customer community
• Build proofs of concept and small-scale deployments to validate solutions and reproduce real-world customer environments
• Act as a trusted advisor to Product Management by delivering actionable feedback informed by real-world field experience
Who You Are:
• You bring a strong software engineering foundation, with hands-on experience building and operating AI Agent-powered applications in production environments at a large scale, and are comfortable diving deep into codebases to understand architectural decisions, tradeoffs, and implementation details
• You are familiar with distributed systems and core observability concepts, such as tracing, metrics, and logging
• You can translate the industry patterns into the emerging AI Agent observability domain, including agentic workflows, LLM spans, experiments, evaluations, and prompt templates
• Experience with languages such as Python and/or JavaScript/TypeScript
• Comfortable operating in rapidly evolving, ambiguous technical domains
• You build deep context across teams and translate it into reusable, scalable solutions
• You take ownership from problem definition through implementation and measurable outcomes
• Highly detail-oriented, particularly when working on archite