Note: The job is a remote job and is open to candidates in USA. Mercury is a company making a significant commitment to AI, aiming to transform isolated experiments into shared infrastructure for enhanced collaboration. The Senior Software Engineer - AI Engineering will help extend and scale the internal AI platform, enabling faster prototyping and iteration across the company while ensuring a strong knowledge layer for AI systems.

Responsibilities

• 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
• 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
• 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
• Build playgrounds and evaluation harnesses so internal AI agents can be tested and iterated in controlled environments before hitting production

Skills

• Has 5+ years of backend development experience in complex, production systems—you've built things that other engineers depended on
• Is fluent across programming languages and can navigate platform engineering, infrastructure, and developer tooling without needing a map
• Has hands-on experience building LLM-powered systems—RAG pipelines, agents, eval frameworks—and has shipped at least one of these to production
• Understands the real tradeoffs in AI deployments: cost modeling, observability, latency, and safety—not just the exciting parts
• Is high-agency and self-directed. You can operate effectively without tightly-defined scope, find the highest-leverage work, and get it done
• Communicates clearly across technical and non-technical audiences—you can explain what you built and why it matters

Benefits

• The total rewards package at Mercury includes base salary, equity, and benefits.
• Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry.
• New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

Company Overview

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