Elloe Ai

Elloe Ai

Technical Product Intern (Compliance x LLM Ops)

Austin, USA · Intern · Internship

Sponsorship not specifiedDetected 376 days ago
ExpressLLMsLLMOpsComplianceWireframingLogisticsFirewallHIPAA

About the role

  • This role is built for a technical product thinker who wants to help define what trust looks like in AI systems.

Responsibilities

  • Exposure to enterprise GenAI design constraints Referenceable work across dashboards, demos, and safety layers A tangible contribution to the infrastructure that keeps
  • You'll partner with engineers, safety leads, and comms to make sure Elloe's products don't just work - they scale with clarity and precision.
  • Interface & Safety UX Prototyping Design or wireframe user-facing safety elements: audit overlays, policy badges, drift alerts Build demo views or product mockups for partners and buyers 2.

Requirements

  • That must be understood, trusted, and provable.
  • Experience with LLM workflows, LangChain, or infra stacks Why This Matters Elloe's core differentiation is real-time enforcement.

Nice to have

  • We protect enterprise AI from hallucinations, bias, and policy breaches - in real time.
  • Our platform powers deployments at Google, Johns Hopkins, the EU Commission, and a Top-5 European Bank.
  • About the Role This role is built for a technical product thinker who wants to help define what trust looks like in AI systems.
  • You'll be prototyping safety overlays, translating policy into product logic, and mapping the competitive edge of our infrastructure stack.
  • Competitive Analysis Break down features and positioning of adjacent tools (Vanta, CalypsoAI, Credo) Help shape differentiation around real-time enforcement vs passive monitoring 3.

Benefits

  • Competitive stipend
  • Competitive stipend Location: Remote-first; timezone overlap with EU or East Coast ideal To Apply: Share your resume and a short note on how you'd simplify the compliance UX for model-based APIs.

This listing is sourced directly from Elloe Ai's careers page and normalized into a canonical job model.