Staff AI Enablement Engineer, Vendelux

• 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
• 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

Desirable

• 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
• 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
• 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
• 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
• 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
• 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