Staff AI Enablement Engineer

Role Description

AI is no longer an engineering story at Vendelux. It's a company story. The way our sales team prospects, the way product researches, the way ops runs processes, the way data answers questions. All of it is changing fast. We're looking for a Staff AI Enablement Engineer to be the technical force behind that change.

This role sits at the intersection of deep AI infrastructure and cross-functional impact. You'll build the MCP servers, agent pipelines, context frameworks, and orchestration layers that make AI genuinely useful across every team - not just for the engineers who've already figured it out on their own. The work is hands-on and technical. The impact is company-wide.

If you're the person at your current company who got everyone on Claude Code, wired up MCP servers for internal tools, and is already running agents 24/7, you might be exactly who we're looking for.

What You'll Do

• 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
• 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
• 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
• 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

Qualifications

• 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

Nice to Have

• 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

• 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

Why This Role, Why Now

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.