VP of Platform Engineering

Interface.ai is building the AI infrastructure for financial services — bringing agentic AI and conversational AI to the credit unions and community banks that serve everyday Americans. We're not a lab, we're not a demo company, and we're not burning runway on hypotheticals. We are in production, generating real revenue, and on a mission that actually matters: democratizing financial wellness for the millions of people who've never had a private banker.

A company with ~$30M in contracted ARR and already cash-flow positive, we're at the inflection point — proven product, paying customers, and a team ready to scale. The next chapter is building the engineering organization that can take us there.

You own the platform-engineering half of the org — the AI-native core that everything runs on: the Intelligence domain (agent runtime, knowledge / retrieval, evals, the data flywheel), Connectivity (integrations, channels, computer-use / actions-beyond-APIs), Data & Fraud, Assemble (the agent-authoring platform), and the cross-pillar infrastructure. This is the AI-native specialist seat: the leader must be deep in agents, LLM orchestration, and the infra that makes them reliable at scale, and must build the platform org and its quality bar. A player-coach who is still in the code, partnering Bruce on architecture.

Org design & growth for the platform domains — build and scale the platform / AI / infra team and its standards.

The agentic & conversational AI platform — LLM orchestration, retrieval systems, evals, and integration / computer-use infrastructure.

Velocity & quality — the tooling, eval gates, and reliability practices every domain depends on.

AI-native engineering culture — frontier tools as standard; an engineering harness that makes every engineer 10×.

Eng/ops excellence — incident response, observability, reliability targets; partner Bruce on architecture and Srinivas on product.

10+ years engineering; 4–6 in leadership at high-growth startups / scale-ups; scaled a platform / infra team through a funding transition.

Domain commonality (required): AI / agentic systems, LLM / ML infrastructure, or conversational / voice AI at production scale working on the platform.

Preferred: ex-founder who scaled an AI-native / platform startup (strong preference, not a bar).

Deep AI engineering fluency — how LLMs work, how to build reliable agentic systems on them, what “agentic AI” means at the infra level.

Hands-on platform background — distributed systems, API design, cloud architecture, production AI ops. Production-scale TypeScript and/or Python.

Still technical — reviews PRs, makes architecture calls, holds their own with a Staff / Chief Engineer. BS/BA in CS required; MS/PhD a plus.