Staff AI Enablement Engineer

About the role

The AI Enablement Engineer will build the shared AI infrastructure layer, design, build, and maintain MCP servers, establish context engineering standards, and orchestrate multi-agent workflows across various functions. They will also partner with different teams to identify and implement AI solutions that improve their work processes.

Responsibilities

• Build the shared AI infrastructure layer
• Design, build, and maintain MCP servers that connect internal systems to agents running across every function
• Establish and own context engineering standards: CLAUDE.md / AGENTS.md conventions, shared context/ directories, architecture docs
• Build the memory and persistence layer for long-running agents: session continuity, proactive scheduling, cross-session context
• Own orchestration infrastructure for multi-agent workflows: coordination, sub-agent spawning, token budgets, permission boundaries
• Maintain codebase health as the system scales: shared component libraries, automated quality gates, fragmentation checks, doc validation in the PR pipeline
• Partner with sales, ops, product, marketing, legal, and data teams to identify where AI can fundamentally change how a team works and build the agents that make it happen
• Create visibility and healthy competition around AI usage: leaderboards, showcases, Slack channels, all-hands demos
• Build and grow a skills marketplace where anyone can package a workflow and share it company-wide
• Drive adoption across every function
• Build purpose-built agents for non-engineering teams
• Own the hard infrastructure problems
• Manage the access vs. safety tension: permissions scoping, token budgets, rate limiting, observability dashboards
• Maintain reliability across agent infrastructure as the system grows: graceful degradation, fallback models, cost tracking
• Evaluate frontier models, new MCP tooling, emerging agent frameworks, and integrate what's worth integrating before competitors catch up

Requirements

• Strong software engineering fundamentals
• Practical, production experience building with LLM APIs
• Experience with proactive/scheduled agent systems
• Familiarity with vector stores, RAG pipelines, or knowledge graph approaches for agent context
• Experience with CI/CD automation involving AI agents
• Exposure to B2B SaaS data models, CRM/MAP integrations, or event/attendee data

What “Staff” Means

A Staff AI Enablement Engineer identifies the highest-leverage problems across the company without being told what they are, defines the technical direction for AI infrastructure and holds the standard across teams, operates with wide autonomy and is accountable for outcomes, not just execution, and brings other engineers along: mentoring, documenting, setting patterns others can follow.