Staff AI Enablement Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Build and scale core AI infrastructure that enables cross-functional teams to leverage AI across Vendelux, designing MCP servers, context frameworks, and agent orchestration while driving adoption across sales, marketing, product, and data teams to maximize event-driven growth.

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build the shared AI infrastructure layer: design, build, and maintain MCP servers that connect internal systems to agents across functions; establish context engineering standards and architecture docs; build memory/persistence for long-running agents; own orchestration for multi-agent workflows; maintain codebase health as the system scales.
•Drive adoption across every function: partner with sales, ops, product, marketing, legal, and data teams; create preconfigured environments and a skills marketplace; drive visibility and internal showcases to spread AI usage.
•Build purpose-built agents for non-engineering teams: compose agents with appropriate integrations, memory, and workflows tailored to diverse users like engineers, sales, and ops.
•Own hard infrastructure problems: manage access/safety, ensure reliability, evaluate frontier models and MCP tooling, and implement guardrails and cost tracking.
•Collaborate across teams to identify leverage points and mentor others, establishing patterns and documentation to scale AI-enabled workflows.

Key Requirements

•Strong software engineering fundamentals. You're building real infrastructure that teams depend on, not just configuring 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 and working cross-functionally with sales, product, marketing, and data teams.

Skills:CommunicationProblem-solvingCollaboration

Tech Stack:Claude CodeCodexLLM APIsMCPGitHubSnowflakeNotionSlackAgent frameworksContext engineering

Company Brief

Vendelux

Operates an e-commerce platform selling consumer home and lifestyle products, providing online ordering, fulfillment, and customer support through a curated digital storefront aimed at retail consumers.

Industry: E-commerce

Website

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Vendelux

3 months ago

Staff AI Enablement Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build the shared AI infrastructure layer: design, build, and maintain MCP servers that connect internal systems to agents across functions; establish context engineering standards and architecture docs; build memory/persistence for long-running agents; own orchestration for multi-agent workflows; maintain codebase health as the system scales.
•Drive adoption across every function: partner with sales, ops, product, marketing, legal, and data teams; create preconfigured environments and a skills marketplace; drive visibility and internal showcases to spread AI usage.
•Build purpose-built agents for non-engineering teams: compose agents with appropriate integrations, memory, and workflows tailored to diverse users like engineers, sales, and ops.
•Own hard infrastructure problems: manage access/safety, ensure reliability, evaluate frontier models and MCP tooling, and implement guardrails and cost tracking.
•Collaborate across teams to identify leverage points and mentor others, establishing patterns and documentation to scale AI-enabled workflows.

Key Requirements

•Strong software engineering fundamentals. You're building real infrastructure that teams depend on, not just configuring 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 and working cross-functionally with sales, product, marketing, and data teams.

Skills:CommunicationProblem-solvingCollaboration

Tech Stack:Claude CodeCodexLLM APIsMCPGitHubSnowflakeNotionSlackAgent frameworksContext engineering

Company Brief

Vendelux

Industry: E-commerce

Website