Staff Engineer, AI Platform – Architecture

🕒 Julho 25

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $190.000 - $290.000 / ano

⏰ Tempo Integral

🔴 Especialista

🤖 Engenheiro de IA

👻 Score fantasma 0%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

Candidatar-se

Encontrar Vagas Remotas Similares

Configurar Alerta de Email para Novas Vagas

📊 Verifique sua pontuação de currículo para esta vaga

Melhore suas chances de conseguir uma entrevista verificando sua pontuação de currículo antes de se candidatar.

Enviar Currículo

Shield AI

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2015

🤖 Inteligência Artificial

🚀 Aeroespacial

🎖️ Defesa

Artificial Intelligence • Aerospace • Defense

A Shield AI é uma das principais desenvolvedoras de soluções militares impulsionadas por IA, focada em aprimorar a autonomia das missões e a consciência situacional no campo de batalha. Sua plataforma, Hivemind, permite a rápida implantação de sistemas inteligentes para diversas aplicações de defesa, incluindo operação de drones e vigilância. Com um compromisso em utilizar tecnologia avançada, a Shield AI visa proteger membros do serviço e civis, revolucionando tecnologias de defesa por meio de sistemas autônomos.

Descrição

• Define and evolve enterprise AI architecture patterns for LLM integration, retrieval-augmented generation, agentic workflows, prompt orchestration, and workflow automation. • Create reference architectures, design reviews, decision records, and implementation guidance that enable consistent AI development across business units. • Serve as a technical authority for AI platform decisions, including model selection, integration approaches, data boundary enforcement, and lifecycle management. • Evaluate emerging AI technologies and recommend fit-for-purpose adoption paths aligned to security, operational, and enterprise architecture requirements. • Partner with product, platform, and business technology teams to identify common needs and convert them into reusable engineering patterns. • Design and build reusable AI components such as connectors, agents, skill templates, prompt libraries, data pipelines, integration adapters, and service APIs. • Lead technical design for shared platform services for AI observability, logging, usage metering, evaluation, and lifecycle management. • Establish quality, versioning, deprecation, documentation, and contribution standards for the shared AI component catalog. • Guide teams through adoption of shared components, balancing standardization with practical implementation needs. • Identify opportunities to eliminate duplicate AI engineering efforts through consolidation, abstractions, and platformization. • Architect engineering controls for access management, data classification enforcement, prompt safety, output validation, audit logging, and policy adherence. • Partner with Security, Legal, and compliance stakeholders to embed responsible AI requirements into development and deployment pipelines. • Design model and agent lifecycle governance patterns, including version tracking, evaluation, drift monitoring, rollback, and deprecation workflows. • Build technical dashboards and telemetry that expose adoption, risk, performance, and governance compliance across AI-enabled systems. • Represent engineering considerations in AI governance reviews and translate policy requirements into implementable technical standards. • Develop AI-assisted workflow patterns that improve individual productivity, team collaboration, knowledge retrieval, meeting intelligence, document generation, and task automation. • Design measurement approaches that connect AI usage to time savings, quality improvement, error reduction, capacity creation, and business value. • Partner with Finance and platform teams to develop cost metering, showback/chargeback, and optimization mechanisms for AI services. • Mentor senior and mid-level engineers, raise engineering quality, and lead complex cross-functional technical initiatives from concept through production. • Contribute to communities of practice, internal enablement material, and technical evangelism for enterprise AI engineering standards.

🎯 Requisitos

• Progressive experience in enterprise software engineering, AI platform engineering, data platform engineering, or digital workplace technology roles. • Deep hands-on understanding of generative AI, large language model integration, RAG architectures, agentic AI patterns, prompt orchestration, and production AI system design. • Experience designing shared platform services, reusable component libraries, APIs, integration frameworks, or developer enablement platforms used by multiple teams. • Strong architecture judgment across security, reliability, scalability, observability, maintainability, and operational cost tradeoffs. • Experience implementing or contributing to AI governance controls such as access management, data classification, audit logging, model lifecycle management, and compliance-aware development practices. • Ability to influence technical direction across matrixed teams through architecture reviews, written guidance, reference implementations, and hands-on collaboration. • Experience defining metrics, telemetry, or attribution mechanisms for adoption, productivity, cost, quality, or operational performance. • Strong written and verbal communication skills with the ability to explain complex AI engineering concepts to technical and non-technical audiences.

🏖️ Benefícios

• Pay within range listed + Bonus + Benefits + Equity • Temporary benefits package (applicable after 60 days of employment)