> Markdown version of [/jobs/ext/3959046-software-engineer](https://www.wearedevelopers.com/jobs/ext/3959046-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - **Company:** FRUITION EXECUTIVE SEARCH LLC - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Systems Integration, Caching, Build Management, Build Tools, Data Pipelines - **Published:** October 27, 2026 - **Apply:** https://www.thejobnetwork.com/job/d7d58eee-2b9f-4f57-8b1a-2237cc1c5eea/software-engineer ## About the Role * Proven track record shipping production systems real users depend on. * Full-stack comfort - you pick up whatever the problem requires. * Deep thinking about reliable retrieval pipelines. * Pragmatism about models: frontier, fine-tuned, or simpler - whatever fits. * Strong eval discipline; you measure impact on real conversations before shipping. * Experience where your work directly shapes the product ## Description The build: Our client is building full-stack AI systems for hospitality operations and guest communication. As an engineer on a small team with end-to-end product ownership, you'll own retrieval, tool orchestration, response generation, and production reliability - from the model-routing layer down to the infra. This is an agent-reliability role: real conversations, messy data, third-party APIs that fail mid-flow. You make it work anyway. What you'll do * Design and build the response generation pipeline - routers, orchestrators, synthesizers that classify intent, assemble context, select tools, and decide whether to send or escalate. * Develop knowledge retrieval that grounds responses in accurate, complete, current data despite messy inputs. * Build tool orchestration layers that execute multi-step workflows reliably, with graceful handling of API failures mid-conversation. * Create evaluation frameworks that measure quality against real conversations and catch regressions pre-customer. * Implement observability and tracing that makes agent reasoning legible to non-technical support teams. * Design no-code controls that let support leads adjust agent behavior. * Extend the system to voice - real-time transcription and the same tool-use capabilities as text. * Optimize speed and cost via model routing, caching, and prompt refinement. * Own infra, data pipelines, integrations, and API work.