Senior AI Platform & Agentic Infrastructure Engineer

United States Digital Space LLC; San Jose (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

United States Digital Space LLC is seeking an engineer to take our AI-enabled Internal Audit platform from prototype to regulator-grade production. You will own the foundation, the agent runtime, and the harness, building a resilient cloud stack that supports multi-model AI and rigorous controls.

In your first year, you will migrate Hive Mind to a high-availability AWS or GCP environment, ensure provenance, and integrate with enterprise data infrastructure while preserving daily workflows and

Qualifications

• Experience building production-grade data pipelines and AI-enabled tooling.
• Ability to design and operate cloud-based, compliant platforms.

Responsibilities

• Inherit and migrate the prototype estate to production without interrupting daily workflows.
• Architect Hive Mind into a resilient cloud platform with HA, DR, and governance.

Job description

Who We Are

At the company, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. the company is a leading crypto exchange, and the developer of the company Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). the company is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. the company is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products the company, the company Wallet, OKLink and more.

About the Opportunity

the company’s Internal Audit function has an early but working AI-native capability: a multi-agent platform (Hive Mind), agentic workflows, data pipelines, and AI-enabled tools that let a very small team punch far above its weight. Your job is not to rebuild it at today’s maturity. Your job is to take it to a regulator-grade production standard and well beyond, and to drive AI Enablement across the department. You own the foundation, the agent runtime, and the harness: a resilient cloud platform, the agentic runtime and evaluation harnesses that make agents trustworthy in a regulated setting, and the governed data and AI infrastructure everything else depends on. We hire on demonstrated building, not on claims or credentials. We expect you to be more capable than the hiring manager in your domain: you will co-own and challenge the tooling strategy, not just implement it. This is a two-person engineering team: you deploy, debug, and hotfix your counterpart’s stack when needed.

Environment

Cloud on AWS or GCP. Claude as the starting point in a deliberately multi-model architecture (Anthropic, OpenAI, Google), using the right model for each job, including the plugins and integrations you build. Google Workspace for reporting and evidence. JIRA for the audit team’s workflow. Lark for team communications, alert bots, and the corporate wiki. You inherit a working Google-native prototype estate (Apps Script web apps, Drive-synced automation, locally scheduled jobs, Claude Code agent tooling) and evolve it without breaking daily use. Infrastructure-as-code, CI/CD, and observability throughout. Treat this stack as the starting point, not a constraint: you build production-grade systems end to end on what exists today, and you are expected to propose, prove, and adopt better components as demand and capabilities evolve. Production-grade here means service levels sized for an internal assurance platform: board-cycle windows are sacred, recovery is measured in hours, and this is not a 24/7 pager culture.

In your first year

• Hive Mind runs in the cloud with HA, DR, SLOs, and audit logging that passes an internal controls review.
• A governed data foundation with provenance and lineage is live across multiple audit domains, integrated with the company's group data infrastructure where it exists.
• An agentic runtime and harness with evaluation and red-teaming gates what reaches production.
• The hiring manager is out of the operational loop: no scheduled job runs on a personal machine, every system has a runbook and a non-founder owner. Decommissioning the founder’s laptop as infrastructure is a literal milestone. When these goals compete, the priority order is: keep the estate alive, then the cloud migration with observability, then the harness gating production, then the data foundation.

How we assess demonstrated building

We assess demonstrated building in ways that respect your time and your confidentiality obligations to current and former employers: a portfolio deep-dive (walk us through systems you built and kept running, at the level of detail your obligations allow; we want architecture, decisions, and trade-offs, never proprietary code, data, or documents), a short, time-capped build exercise on a synthetic problem unrelated to the company's business (a small agentic workflow with an evaluation harness, used for assessment only and never put to use by the company; the work remains yours) that you defend live, walking us through your design decisions and extending it on the spot, a systems-design session on taking a prototype estate to production, and references focused on whether you built and operated systems in production.

Trust and compliance

This role handles highly sensitive audit data at a global crypto exchange. Expect background checks, confidentiality obligations, and personal-trading and material-non-public-information

What You’ll Be Doing

• Inherit, operate, and progressively migrate the working prototype estate (Google Workspace-native automation across Apps Script, Drive, and the Docs/Sheets/Slides APIs; locally scheduled jobs; and Claude Code agent tooling) to the target platform without interrupting daily and board-cycle workflows. Working software wins arguments; migrate by strangling, not rewriting. Reuse and integrate with the company's prevailing and evolving AI capabilities and data infrastructure, including enterprise-approved models and gateways, Model Context Protocol (MCP) servers and connectors, security tooling, and group data platforms, before building parallel capability.
• Re-architect Hive Mind into a resilient AWS or GCP platform with high availability (HA), disaster recovery (DR), defined service