Mercury Senior Software Engineer on AI Engineering team building and scaling Mercury's internal AI platform and enablement layer. Extend AI infrastructure and enable faster prototyping across the company.
Responsibilities
You'll join a team that has already started building Mercury's internal AI platform and enablement layer. Your work will be to extend, harden, and scale what's in motion, and to help partner teams adopt it.
Extend the AI platform foundation
Build and evolve MCP servers that connect internal systems and data sources into a coherent interface for agents and engineers.
Expand and operate our LLM gateway infrastructure: routing, rate limiting, cost attribution, and observability across teams.
Turn early patterns into durable defaults: shared prompt libraries, guardrails, and policy-as-code so teams can move fast safely.
Strengthen the shared company knowledge layer
Shape and maintain structured context artifacts—clean, reliable, agent-consumable—so LLMs working in Mercury's systems can reason accurately about our domain.
Improve internal knowledge discoverability and retrieval so both humans and agents can quickly find accurate answers.
Partner with domain teams to standardize key sources of truth, and keep them fresh.
Enable faster prototyping and iteration across the company
Build and refine sandbox environments and tooling that let engineers experiment with AI safely and at speed.
Create self-service scaffolding so non-engineers—PMs, ops, finance—can prototype and deploy AI-powered workflows with minimal hand-holding.
Qualification
Required
5+ years backend development experience in complex production systems
Fluency across programming languages and platform engineering
Hands-on experience building LLM-powered systems and shipping to production
Understanding of AI deployment tradeoffs including cost, observability, latency, and safety
