Represent the trustworthiness and confidence of serious expertise in practical AI applications in customer conversations (especially as an escalation point above an SE SME)
Spearhead the early-stage implementation and adoption of LD4AI at scale, working closely with existing customers to ensure activation and churn prevention (alongside feedback)
Develop and document Solutions Engineering playbooks and best practices that scale beyond our first customers
Function as a tremendous partner to the LD4AI Chief of Staff, surfacing revenue-related insights with respect to the maturation of the product accordingly
Support LD4AI POVs ran by on-the-ground SEs
Collaborate extensively with product and engineering teams to ensure product concepts are technically feasible and align with LaunchDarkly's strategic goals
Drive continuous improvement by monitoring product performance, user experience, and market response, iterating based on actionable data and insights
Confirm that LD4AI’s roadmap adheres to market demand and deliver exceptional user experiences, especially for the AI developer persona – vocalizing objections with data
Deliver integrations against common, quantified customer requests in the form of code contributions, architecture diagrams, and whitepapers
Function as a key technical asset in (with the Head of AI and the partnership team) technical partnerships with advantageous potential partners (like Anthropic, DataBricks, etc…)
Publicly evangelize LD4AI at mainstream industry conferences, webinars, partner engagements, and strategic meetings
Function as the primary owner of the disbursement of enablement material to the SE team, particularly to the designated by team/region SE LD4AI SMEs (for which you have partial, dotted-line accountability)
Co-own Revenue Enablement of LD4AI along with the Head of AI and the LD4AI tetrad
Work with the AI Researcher, the PMM team, and the Head of AI to build and maintain competitor playbooks
Requirements
Minimum of 12 years of related technical experience
Extensive experience with AI applications—building, implementing, or selling AI solutions at scale
Experience building multi-agent systems using frameworks like LangGraph, AgentBuilder, or AgentCore
Hands-on experience evaluating AI agent performance at scale using automated evaluation methods
Deep understanding of LLM mechanics (you've read 'Attention is All You Need' and can explain transformer architecture in detail)
Experience building or interfacing with MCP (Model Context Protocol) servers
Strong Python skills with experience building in PyTorch or TensorFlow
Strong foundation in software engineering principles and current market trends.