Full-Stack Engineer (AI Product + Client Solutions) ABOUT USEmberos exists to bring predictive intelligence and clarity to a world where AI is already shaping decisions.We help brands understand how AI interprets and represents them at the moment of discovery and how that understanding compounds over time. By revealing emerging patterns before assumptions harden into belief, we give leaders foresight into what is taking shape and the ability to act early.This is not just monitoring. It’s predictive intelligence designed for an AI-mediated world.As AI increasingly defines what people see, trust, and choose, Emberos serves as a grounding force for truth by bringing clarity to how meaning forms today and how it evolves tomorrow.We’re a small, fast-moving team building something foundational, and we’re looking for people who want to shape what comes next.The OpportunityWe’re looking for a Full-Stack Engineer, AI Product & Client Solutions who can truly own and ship product end-to-end across frontend, backend, and data layers without needing constant oversight. You’ll be responsible for architecting and delivering systems that power our Brand Knowledge Graph, building interfaces that make complex enterprise data intuitive, and developing the LLM orchestration layer so it runs efficiently and reliably at scale.Equally important, you’re comfortable operating autonomously and taking full ownership from concept through deployment. You can build it, explain it, demo it, and defend the technical decisions behind it, translating complex systems into clear narratives for clients without needing to be managed step-by-step.What You'll BuildBRAND KNOWLEDGE GRAPH + DATA SYSTEMSDevelop and extend our Brand Knowledge Graph in Neo4j: modeling how changes to one node propagate and affect AI visibility and recommendations elsewhereBuild predictive optimization systems that model how specific content and strategy changes impact AI visibility outcomesDesign measurement and feedback loops that connect changes to outcomes and track predicted vs. actual lift over timeImplement scoring logic including Share-of-Prompt, accuracy measurement, sentiment analysis, and competitor mention detectionAnalyze how optimizations at scale affect long-term LLM behavior and ecosystem dynamicsLLM ORCHESTRATION + AI INTEGRATIONSBuild and maintain integrations with LLM platforms including ChatGPT, Claude, Grok, Perplexity, DeepSeek, and others as they emergeDesign orchestration systems that minimize unnecessary LLM calls: managing cost, latency, and quality tradeoffs intelligentlyDevelop agent-based analysis workflows that evaluate multiple optimization scenarios in parallel and forecast impactCompare and stress-test multiple optimization strategies simultaneously to surface the most effective approachesFULL-STACK PRODUCT ENGINEERINGBuild and ship full-stack features across frontend, backend, and data layers: from idea to productionDevelop high-quality frontend interfaces in React and TypeScript that translate complex graph and model outputs into actionable insights for usersDesign and optimize backend systems for performance, security, and scalabilityBuild workflow and tracking infrastructure recording what changed, why it changed, and the outcome -- integrating with Jira, Slack, HubSpot, and emailLay foundations for AI-native commerce experiences where merchants can transact directly inside AI chatWork closely with design and product to translate ideas into polished, production-ready experiencesCLIENT-FACING + QAParticipate actively in client calls, demos, and technical conversations -- explaining systems clearly to non-technical enterprise stakeholdersOwn QA processes and fix pack delivery: building test coverage, triaging bugs, and maintaining data integrity across the platformPull insights from complex datasets and translate them into findings that clients and internal teams can act onDocument architecture, decisions, and systems to support a growing team and future CTO onboardingMust-Have Experience5+ years of software engineering experience in fast-moving, high-performance environmentsStrong full-stack engineering experience -- you have shipped real products end-to-end, not just maintained existing onesComfortable operating in client-facing settings. With an ability to clearly explain technical architecture, AI methodology, and product decisions to non-technical stakeholders with confidence.Deep hands-on experience with Neo4j and graph data modelingPractical experience integrating LLMs and building production-grade API integrations with AI platformsAbility to design systems that avoid constant LLM calls and manage cost, latency, and quality tradeoffsProficient in React, TypeScript, Node.js, and modern frontend and backend frameworksExperience designing and working with scalable databases and APIsClear, confident communicator who is energized by client interaction -- not just tolerant of it. With the ability to lead technical demos, respond to live client questions, and translate complex systems into clear business value narratives.Nice to HaveExperience with applied data science, ML-adjacent systems, or experimentation frameworksBackground in AI search, recommendation systems, SEO-like ranking models, or similarPractical agent orchestration experience - deterministic, evaluable, production flowsFamiliarity with conversational commerce or AI-powered transactional flowsExperience working on creative or content-driven platformsWho Thrives HereYou move fast and finish things. You are comfortable owning a feature from whiteboard to production, and you do not need someone to hand you a spec. You bring strong opinions about how to build things well and you are willing to defend them in a conversation, then document them afterward.You get energy from variety. In a given week you might be extending the knowledge graph, debugging an LLM orchestration flow, presenting analysis to an enterprise client, and shipping a frontend improvement.