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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - Agents - **Company:** Mira Mace Medical Services West P.C. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Retrieval-Augmented Generation, Large Language Models, Backend - **Published:** November 3, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-ai-engineer-agents-san-francisco-ca--09336937-34ab-4f84-a872-a078679439eb ## About the Role Artificial Intelligence (AI) Agents, Medicare, Insurance, Mace Experience, Healthcare, Artificial Intelligence (AI) Agents, Nursing, Startup, Investment Capital, Artificial Intelligence (AI), * You've built LLM-powered systems or agents, shipped them to production, and kept them reliable - not demos, but systems real people depend on. * Strong overall AI fluency. You can reason concretely about prompting, tool use, context management, retrieval, latency, cost, and failure modes. * You've built evaluation into your work - designed metrics, captured signal from production, and used it to systematically improve quality. * You have product judgment, and can show it: a time you challenged a spec, cut scope to ship what mattered, changed course after watching real users, or built something nobody asked for that turned out to matter. * Solid software engineering fundamentals. You write code others can maintain, and you own what you ship. NICE TO HAVE * Experience in healthcare or another regulated industry (HIPAA, PII/PHI handling). * Experience as founder / founding team member / early stage startup. ## Description We're looking for an AI engineer who owns a business outcome end to end: deciding what to build, building it, and proving it worked. Engineers take a company goal, find where the leverage is, turn it into a roadmap, ship, and measure whether it moved the number. Our product runs on agents. They decide what work needs to happen for each customer, do a lot of that work autonomously, check the quality of what got done, and assist the people on our team who handle the rest. You'll own that layer. Concretely, you'll build agentic systems that carry multi-step work from start to finish, the evaluation infrastructure that tells us whether they're any good, the retrieval layer that gives them accurate knowledge to reason over, and the AI features our internal users rely on every day. The surface is wide and still being defined, so there's a lot of room to shape what gets built. We don't have a strong opinion about which corner of AI engineering you came from - agents, RAG, evals, fine-tuning, retrieval. What matters is that you're genuinely fluent with modern LLMs, that you treat them as a backend you can build reliable systems on, and that you've shipped something real to real users., * Turn goals into a roadmap. Take a goal like "more signups reach their first visit" or "claims get paid faster," break it into the problems behind it, size them, and decide what to build first. You write the plan, defend the tradeoffs, and change it when the data says to. * Build agents that do real work. Design and ship LLM-powered systems that take a multi-step task and see it through - deciding what to do, taking action across our systems and third parties, and knowing when to hand off to a human. * Own quality and evaluation. Build the infrastructure that measures how well our AI performs, catches regressions before users feel them, and turns production signal into better prompts, retrieval, and models. Make quality a number we can move, not a vibe. * Build AI into the product. Ship copilots and assistive features that make our users meaningfully faster, with clear judgment about when the model should act and when it should ask. * Close the loop on real usage. Deploy to production, watch how the systems behave with real users, and improve from what actually happens - in days, not quarters. Success is the business metric the work was meant to move, not the ship date. * Lay the foundation for scale. Make architectural decisions that hold up at 100× usage., Everything we do is guided by a set of leadership principles that define how we operate: * Patient First. We start with the patient and work backwards; every decision is filtered through whether it makes the patient's life better. * Sense of Urgency. Every day a patient goes unnavigated is a day someone couldn't get help, so we move fast and decide with conviction. * Ownership. We own outcomes, not tasks, and never say "that's not my job." * Insist on the Highest Standards. In clinical quality, operational execution, and how we show up for patients and partners - and we keep raising the bar. * Question Everything, Unapologetically. We reason from first principles, challenge requirements regardless of who set them, and dig to the root of the problem.