Vendelux - Staff AI Enablement Engineer
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Requirements
• Technical depth is the baseline. The ability to move others up the proficiency curve is what makes you exceptional in this role. • Strong software engineering fundamentals. You're building real infrastructure that teams depend on, not configuring existing tools • Deep hands-on experience with frontier AI agents (Claude Code, Codex, or equivalent) and the context engineering that makes them actually useful in complex codebases • Practical, production experience building with LLM APIs: tool use, multi-turn state, system prompt architecture, structured outputs, multi-agent orchestration • Hands-on experience with MCP or similar integration frameworks. You've connected agents to real production systems, not just toy examples • Experience designing for non-technical users: the agent that works for a software engineer is not the same as the one that works for a sales rep or an ops manager • Comfort working cross-functionally. You'll spend as much time talking to a head of sales or a product lead as you will writing code • Background in platform engineering, developer tooling, or data engineering • Experience with proactive/scheduled agent systems (not just request-response) • 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 Here • Staff isn't a senior role with a better title. It means: • You identify the highest-leverage problems across the company without being told what they are • You define the technical direction for AI infrastructure and hold the standard across teams • You operate with wide autonomy and are accountable for outcomes, not just execution • You bring other engineers along: mentoring, documenting, setting patterns others can follow
Responsibilities
• Build the shared AI infrastructure layer • Design, build, and maintain MCP servers that connect our internal systems like Github, Snowflake, Linear, Notion, Slack, and others, to agents running across every function • Establish and own our context engineering standards: CLAUDE.md / AGENTS.md conventions, shared context/ directories, architecture docs that make our agents deeply aware of how Vendelux works • 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 • Drive adoption across every function • 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 • Get every team to their “aha” moment fast: preconfigured environments, pre-connected tools, skills they can run immediately without debugging • Build and grow a skills marketplace where anyone can package a workflow and share it company-wide so one person’s breakthrough becomes everyone’s superpower • Create visibility and healthy competition around AI usage: leaderboards, showcases, Slack channels, all-hands demos that make building contagious • Identify force multipliers on every team (the people who get it early) and give them the platform and resources to bring their teams along • Build purpose-built agents for non-engineering teams • Each agent isn’t a chatbot. It’s a composition: the right MCP integrations, the right document access, the right memory system, the right workflows assembled into something that genuinely serves a function’s real work • Work closely with domain experts to turn institutional knowledge into something an agent can act on; the best agents are co-created, not handed down • Given Vendelux’s focus on event intelligence and pipeline, there’s particular leverage in agents that understand our data models, account scoring, and sales workflows • Own the hard infrastructure problems • Manage the access vs. safety tension: permissions scoping, token budgets, rate limiting, observability dashboards — guardrails that enable rather than block • 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
Benefits
• The gap between AI-native teams and everyone else is widening fast. At Vendelux, we're at an inflection point: our data assets — event intelligence, attendee behavior, account signals — are exactly the kind of domain-specific context that makes AI agents genuinely powerful rather than generic. • The AI Enablement Engineer's job is to make that advantage real across every team. To make intelligence self-service, the same way DevOps made infrastructure self-service. One afternoon of setup, connecting the right agent to the right data and deploying it where a team already works, creates a permanent productivity gain that compounds. • We're looking for the person who already knows this and wants the scope to do it at scale. • Not all candidates will check all of the requirements listed above and that’s ok! We are open to great people from non-traditional backgrounds. • Vendelux is proud to be an equal opportunity workplace. We are committed to equal opportunity regardless of race, color, ancestry, religion, gender, gender identity, parental or pregnancy status, national origin, sexual orientation, age, citizenship, marital status, disability, or veteran status.
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