Enterprise AI Systems Engineer
JUUL Labs · Austin, Texas, United States; Dallas, Texas, United States; San Francisco, California, United States; Washington, District of Columbia, United States
About this role
**Enterprise AI Systems Engineer** **THE COMPANY** Juul Labs’ mission is to transition the world’s billion adult smokers away from combustible cigarettes, eliminate their use, and combat underage usage of our products. They’re committed to exceptional quality, research, design, and innovation. **ROLE AND RESPONSIBILITIES** - Own AI platforms end-to-end—how AI runs at the company, not someone else’s design. - Administer enterprise AI tenants (Claude, Gemini, OpenRouter, Cursor, NotebookLM): user provisioning, groups/roles, workspace configuration, and tenant security settings. - Build and maintain platform content (skills, prompts, projects, connectors, and documentation) and drive adoption across ~250 users and growing. - Track consumption and spend across platforms; report usage, identify idle seats, and support business-unit chargeback. - Keep platforms running: availability monitoring, feature rollouts, user issue triage, and escalation ownership. - Build and operate MCP servers and gateways connecting platforms to internal systems with least-privilege access and full audit logging. - Run the LLM gateway for centralized routing, authentication, rate limiting, logging, and policy enforcement across model providers. - Choose the right model per workload (commercial vs. open-weight) based on cost, latency, capability, and data sensitivity. - Build agentic workflows and automations against internal APIs/tools with evaluation and guardrails. - Architect and run AI workloads on AWS and GCP (compute, networking, IAM, secrets management, private connectivity, Bedrock, Vertex AI). - Own code in GitHub and write most of it yourself (primarily Python and TypeScript), including branch protection, access control, secret scanning, and Actions pipelines. - Instrument the stack for cost, performance, and security telemetry; build reporting for leadership and finance. - Enforce data handling, retention, and access policy with Cybersecurity and AI Governance; write runbooks and standards. **PERSONAL AND PROFESSIONAL QUALIFICATIONS** - 5+ years in software/cloud/platform engineering, including recent hands-on work building and running LLM-based systems. - Experience administering enterprise SaaS/AI tenants at scale (access management, configuration, cost control, adoption). - Working knowledge of commercial and open-weight LLMs (prompt engineering, evaluation, retrieval, agentic patterns). - Hands-on with Model Context Protocol (MCP), MCP gateways, or LLM gateways (comparable API integration/middleware experience). - AWS and GCP architecture experience (IAM, networking, secrets management, managed AI services). - Strong engineering discipline with GitHub (version control, code review, testing, CI/CD, infrastructure as code). - Strong Python and/or TypeScript for integrations, automations, and internal tooling. - Security and data governance fundamentals for AI work (least privilege, data classification, DLP, auditability). - Ability to explain technical work to executives, finance, and non-technical stakeholders. - **Nice to have:** Vertex AI or Amazon Bedrock, self-hosted open-weight model deployment, vector databases/RAG pipelines, Splunk or another SIEM, cloud cost management. **EDUCATION** - Preferred: bachelor’s degree in an applicable field or relevant work experience. **JUUL LABS PERKS & BENEFITS** - Career growth and support to set and exceed big goals. - Work with talented, committed, supportive teammates. - Equity and performance bonuses
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