Enterprise AI Systems Engineer

ROLE AND RESPONSIBILITIES:

• You own these platforms end to end. What you build gets used across the whole company, and you decide how AI runs here rather than inheriting someone else's design.
• Administer our enterprise Claude, Gemini, OpenRouter, Cursor and NotebookLM tenants. User provisioning, groups and roles, workspace configuration, and tenant security settings.
• Build and maintain the content that makes those platforms useful: skills, prompts, projects, connectors, and the documentation behind them. Drive adoption across 250 users and growing.
• Track consumption and spend across all three platforms. Report on usage, find idle seats, and support the business-unit chargeback model.
• Keep the platforms running. Availability monitoring, feature rollouts, user issue triage, and escalation ownership when something breaks.
• Build and operate the MCP servers and gateways that connect these platforms to internal systems, with least-privilege access and full audit logging.
• Run the LLM gateway that centralizes routing, authentication, rate limiting, logging, and policy enforcement across model providers.
• Choose the right model for each workload, commercial or open-weight, based on cost, latency, capability, and how sensitive the data is.
• Build agentic workflows and automations against internal APIs and tools, with the evaluation and guardrails to keep them predictable.
• Architect and run AI workloads on AWS and GCP: compute, networking, IAM, secrets management, private connectivity, Bedrock, and Vertex AI.
• Own the code in GitHub and write most of it yourself, mostly Python and TypeScript. Branch protection, access control, secret scanning, and Actions pipelines.
• Instrument the stack for cost, performance, and security telemetry, and build the reporting that leadership and finance rely on.
• Enforce data handling, retention, and access policy alongside Cybersecurity and AI Governance, and write the runbooks and standards behind it.
• Perform related duties as assigned, within your scope of practice

PERSONAL AND PROFESSIONAL QUALIFICATIONS:

• 5+ years in software, cloud, or platform engineering, including recent hands-on work building and running LLM-based systems.
• Experience administering an enterprise SaaS or AI tenant at scale: access management, configuration, cost control, and adoption.
• Working knowledge of both commercial and open-weight LLMs, including prompt engineering, evaluation, retrieval, and agentic patterns.
• Hands-on with Model Context Protocol (MCP), MCP gateways, or LLM gateways. Comparable API integration and middleware experience counts.
• AWS and GCP architecture, including IAM, networking, secrets management, and managed AI services.
• GitHub and standard engineering discipline: version control, code review, testing, CI/CD, and infrastructure as code.
• Strong Python and/or JavaScript/TypeScript for building integrations, automations, and internal tooling.
• Security and data governance fundamentals applied to AI work: least privilege, data classification, DLP, and auditability.
• Able to explain technical work to executives, finance, and people who do not work in technology.
• Nice to have: Vertex AI or Amazon Bedrock, self-hosted open-weight model deployment, vector databases and RAG pipelines, Splunk or another SIEM, and cloud cost management.

EDUCATION:

• Preferred, bachelor degree in applicable field or relevant work experience

JUUL LABS PERKS & BENEFITS:

• A place to grow your career. We’ll help you set big goals - and exceed them
• People. Work with talented, committed and supportive teammates
• Equity and performance bonuses. Every employee is a stakeholder in our success
• Cell phone subsidy, commuter benefits and discounts on JUUL products
• Excellent medical, dental and vision, disability, and life insurance, plus family support, wellness, legal, and employee assistance program benefits
• 401(k) plan with company matching
• Plus biannual discretionary performance bonuses