AI Forward Deployed Engineer

🕒 Julho 27

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Hiver

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2011

☁️ SaaS

🤝 B2B

🤖 Inteligência Artificial

💰 $22.000.000 Series B - Hiver em 2022-01

SaaS • B2B • Artificial Intelligence

Hiver é uma plataforma de atendimento ao cliente com tecnologia nativa de IA, oferecida como SaaS. Começou como um helpdesk nativo do Gmail e agora oferece o Hiver Omni, uma plataforma omnicanal dedicada que unifica e-mail, chat, Slack, voz, WhatsApp e portais de autoatendimento em um único espaço de trabalho de tickets. A plataforma de IA da Hiver inclui Agentes de IA, Copiloto de IA, Centro de Ajuda de IA, Insights de IA e QA de IA para triagem de tickets, elaboração de respostas, execução de ações em múltiplas etapas através de CRMs/ERPs e outras ferramentas, além de manter o conteúdo da base de conhecimento sempre atualizado. O produto enfatiza a colaboração (caixas de entrada compartilhadas, integração com Slack, notas internas), integrações profundas (Salesforce, Shopify, NetSuite e mais de 10. 000 conectores) e casos de uso em suporte ao cliente, finanças, ITSM e RH. A empresa se posiciona como a plataforma de atendimento ao cliente agêncica construída para suportes complexos e é confiada por mais de 10. 000 equipes.

Descrição

• Own AI Deployments End-to-End • Lead technical onboarding for AI Agents, Copilot, and other AI capabilities • Configure knowledge sources, workflows, automation rules, and AI behaviors • Integrate customer systems and enable production-ready deployments • Define rollout plans and success criteria alongside customer stakeholders • Continuously improve deployment playbooks and implementation best practices • Become the First Line of Technical Investigation • When customers ask: *"Why did the AI respond this way?"* You will: • Investigate AI behavior using logs, traces, evaluation tooling, and retrieval diagnostics • Identify whether issues stem from: Knowledge quality, Retrieval failures, Prompt or workflow configuration, Product limitations • Resolve issues through configuration whenever possible • Escalate to engineering only with well-diagnosed, reproducible problems • Build Custom Solutions • Develop production-quality integrations, scripts, and automations • Build connectors using customer APIs • Perform data migrations and knowledge-base transformations • Prototype customer-specific solutions where product capabilities don't yet exist • Convert recurring workarounds into product improvement proposals • Improve AI Quality • Run structured AI quality reviews using evaluation frameworks • Measure and improve response quality, deflection, and resolution rates • Tune AI behavior using knowledge improvements, workflows, and automation logic • Replace anecdotal feedback with measurable quality metrics • Shape the Product • As someone closest to customer deployments, you'll help influence the product roadmap by: • Identifying recurring deployment friction • Tracking feature gaps and customer pain points • Sharing structured field insights with Product and Engineering • Helping distinguish between product improvements and implementation best practices • Enable Internal Teams • Create deployment playbooks and troubleshooting guides • Train Customer Success teams on AI administration and diagnostics • Reduce dependency on Product and Engineering for routine customer issues • Help scale AI deployments through documentation and operational excellence

🎯 Requisitos

• Full-stack engineering fundamentals — 3–6 years of software engineering experience, able to ship production-quality code independently (Python + TypeScript/JS is the likely fit for our stack). Not a scripter; a real engineer. • APIs & integrations — comfortable reading a customer's API docs, building connectors, moving data between systems (email systems, helpdesks, CRMs, webhooks). • LLM systems literacy — understands RAG pipelines, retrieval failure modes, prompt/instruction design, vector search, and why an AI answer went wrong. Hands-on with eval/observability tooling (we use Langfuse-style tracing, LLM-as-judge, golden datasets — they should be able to read and extend these). • Data skills — strong SQL, comfort with messy customer data, log analysis at scale. • Production debugging — can investigate live issues methodically: reproduce, isolate, root-cause, document. • Cloud/infra basics — enough AWS/GCP to understand deployment constraints, auth (OAuth/SSO), and data security questions customers will ask. • Customer-facing composure — can run a call with a frustrated support ops leader, set expectations honestly, and leave them more confident than before. • Translation both ways — turns vague customer complaints into precise technical diagnoses, and technical constraints into plain business language. • Ownership under ambiguity — thrives with incomplete requirements; scopes an MVP fix, ships it, iterates. Doesn't wait for a ticket to be perfectly specified. • Judgment on escalation — knows the difference between "I can fix this with config" and "this is a product gap engineering must see," and doesn't cry wolf. • Documentation discipline — playbooks, runbooks, and field reports are half the job. If it isn't written down, the time savings don't compound. • Prioritization across accounts — will juggle multiple deployments; needs to manage their own queue without a PM directing traffic. • Maintain work-hour overlap with Engineering, US-based teams, and customers to foster effective cross-functional collaboration. • Nice to Have • Experience working in Customer Support, Helpdesk, or CX SaaS • Previous experience as a Solutions Engineer, Implementation Engineer, Customer Engineer, or AI Forward Deployed Engineer • Experience deploying AI solutions in production for enterprise customers • Familiarity with security, compliance, and data privacy requirements in SaaS environments.