Senior Software Engineer - AI Engineering

Mercury Mercury · Fintech · Remote · Software Engineering

This role focuses on building and scaling Mercury's internal AI platform and enablement layer, extending existing infrastructure, operating LLM gateway, and strengthening the shared company knowledge layer. The goal is to turn scattered AI experiments into shared capabilities, enabling faster prototyping and iteration across the company, including self-service tools for non-engineers and evaluation harnesses for AI agents.

What you'd actually do

  1. Build and evolve MCP servers that connect internal systems and data sources into a coherent interface for agents and engineers.
  2. Expand and operate our LLM gateway infrastructure: routing, rate limiting, cost attribution, and observability across teams.
  3. Turn early patterns into durable defaults: shared prompt libraries, guardrails, and policy-as-code so teams can move fast safely.
  4. Shape and maintain structured context artifacts—clean, reliable, agent-consumable—so LLMs working in Mercury's systems can reason accurately about our domain.
  5. Build and refine sandbox environments and tooling that let engineers experiment with AI safely and at speed.

Skills

Required

  • 5+ years of backend development experience in complex, production systems
  • Fluent across programming languages
  • Hands-on experience building LLM-powered systems—RAG pipelines, agents, eval frameworks
  • Shipped at least one LLM-powered system to production
  • Understands the real tradeoffs in AI deployments: cost modeling, observability, latency, and safety
  • High-agency and self-directed
  • Communicate clearly across technical and non-technical audiences

Nice to have

  • platform engineering
  • infrastructure
  • developer tooling

What the JD emphasized

  • extend, harden, and scale what's in motion
  • shared infrastructure, shared context, and shared capability
  • move fast safely
  • reason accurately about our domain
  • experiment with AI safely and at speed
  • prototype and deploy AI-powered workflows with minimal hand-holding
  • tested and iterated in controlled environments before hitting production
  • complex, production systems
  • shipped at least one of these to production
  • real tradeoffs in AI deployments

Other signals

  • building internal AI platform
  • extend, harden, and scale what's in motion
  • partner teams adopt it
  • shared infrastructure, shared context, and shared capability
  • multiplier effect