Software Engineer, AI Systems

🕒 Setembro 28

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🤖 Engenheiro de IA

👻 Score fantasma 22%

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AI Fund

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2017

💼 Consultoria

🏥 Saúde

🏭 Manufatura

Consulting • Healthcare • Manufacturing

O AI Fund é um venture studio focado em lançar empresas baseadas em IA que buscam impulsionar o progresso em diversos setores. Ao se associar a fundadores talentosos e pioneiros em tecnologia, o AI Fund apoia o desenvolvimento de aplicações inovadoras de inteligência artificial, abrangendo indústrias como manufatura, saúde mental, educação e outras. A organização atua como um cofundador minoritário, ajudando a cultivar startups que aproveitam o potencial transformador da IA para melhorar o mundo.

Descrição

• Build and operate multi-step LLM pipelines coordinating model calls, tool calls, graph queries, retrieval, quality gates, and specialist-agent handoffs • Extend Haven’s coordinated agent team and orchestration layer from incident evidence through analysis, review, and enterprise learning • Design the context layer across Neo4j graph traversal, vector search, and hybrid retrieval • Build evaluation datasets, scoring systems, regression suites, model comparisons, human-label loops, and per-stage quality attribution • Implement tracing, tool-call audits, cost and latency monitoring, failure handling, and quality dashboards • Detect loops, hallucinations, and silent drift before customers do • Select models across OpenAI, Anthropic, and Google based on task requirements • Partner with product and knowledge engineering • Help shape the AI roadmap • Design, ship, instrument, and improve AI/LLM reasoning systems using production evidence • Report to the CTO

🎯 Requisitos

• AI or ML engineering experience, including shipping LLM systems that real users depend on • Hands-on experience building and debugging multi-step, tool-calling workflows with LangGraph, LangChain, or an equivalent framework • Repeatable approach to LLM evaluation, including representative datasets, regression testing, LLM-as-judge techniques, or human review loops • Experience assembling context for LLMs and a clear point of view on what to retrieve, how much, and why • Track record of owning systems from deployment through monitoring and incident response, including diagnosing and fixing a failure or regression • Comfort working across model providers and explaining tradeoffs in quality, latency, cost, context, and operational risk • Comfort with Neo4j and Cypher, or a comparable graph store, and ability to ramp quickly on graph data modeling (strongly preferred) • Strong Python • Production experience with FastAPI, asynchronous services, testing, observability, and maintainable interfaces • Cypher fluency, schema evolution, MERGE patterns, embeddings, and operating a live knowledge graph (nice to have) • Prompt-injection awareness, context-leak prevention, tenant isolation, role-based access, and policy-layer separation (nice to have) • Experience running production AI services in Azure and using Azure AI Search, Pinecone, MongoDB Atlas, pgvector, Elasticsearch, or a similar platform (nice to have) • Prior experience operating in an enterprise-level environment (strongly preferred) • Must be based in the US; relocation will not be considered

🏖️ Benefícios

• Meaningful reasoning problems • An evaluation-first culture • Visible customer impact • Small team, high ownership • Direct feedback from safety teams and users • Opportunity to work closely with the CTO, product, and knowledge engineering • Opportunity to make consequential technical decisions and see work reach customers quickly