AI Engineer, Senior

Vaga não está no LinkedIn

🕒 Outubro 28

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

👻 Score fantasma 13%

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Logiciel Solutions | AI-Powered Teams That Feel In-House

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2011

🤖 Inteligência Artificial

☁️ SaaS

🤝 B2B

Artificial Intelligence • SaaS • B2B

A Logiciel Solutions é uma empresa de engenharia de software focada em IA que integra equipes de desenvolvimento de alto desempenho e habilitadas para IA nos fluxos de trabalho dos clientes para entregar aplicativos web e móveis, agentes de IA e sistemas nativos da nuvem. Eles oferecem serviços que incluem IA agentica/autônoma e agentes de IA para fluxos de trabalho de produtos e engenharia, desenvolvimento de MVP e SaaS, design de UX/UI, automação de QA e testes, engenharia de dados para prontidão de IA e engenharia de nuvem/DevOps (AWS). A Logiciel tem como alvo líderes em tecnologia e equipes de produto em setores como Imobiliário/PropTech, Saúde, FinTech/Produtos Financeiros, Martech, Construção e Melhorias Domésticas, oferecendo equipes prontas para entrega, orientação em arquitetura nativa de IA e extensos playbooks e whitepapers para adoção de engenharia e IA.

Descrição

• Design and implement multi-step AI agents, reasoning flows, and automation workflows interacting with internal and external systems. • Implement agent logic, tooling, and workflow orchestration. • Develop retrieval pipelines and knowledge-grounding workflows. • Build structured extraction flows using LLMs and transformation logic. • Convert prototypes into production-ready services with monitoring and predictable behavior. • Ensure agents are explainable, observable, and have defined failure and fallback paths. • Define architectures for applied AI solutions using TypeScript/Node or Python. • Integrate agents with APIs, databases, event systems, and existing product surfaces, including protocols such as MCP or A2A. • Establish patterns for prompt management, model selection, retrieval, and verification. • Implement evaluation pipelines to monitor accuracy, drift, and reliability. • Build reusable modules and internal tooling to accelerate future AI development. • Build functional prototypes and demos to validate ideas. • Assess feasibility, complexity, and risks of proposed AI features. • Collaborate with sales and delivery teams on scope and technical approach. • Advise on trade-offs between model and architecture choices. • Help create realistic timelines and delivery plans. • Mentor backend engineers on AI patterns, workflows, and integration techniques. • Introduce practices for testing, evaluation, observability, and versioning. • Share knowledge on prompting, model behavior, and tool usage. • Establish standards for applied AI solution design and delivery.

🎯 Requisitos

• 5+ years of experience stated in the experience range. • 6–15 years of software engineering experience with strong backend fundamentals. • Proven experience building and shipping applied AI systems or agents in production. • Strong hands-on experience with TypeScript/Node and Python. • Experience with agent frameworks or custom orchestration patterns. • Strong understanding of retrieval workflows, embeddings, chunking, and evaluation methods. • Experience working with vector databases and related tooling. • Decent understanding of underlying infrastructure and production operations, including LLMOps. • Ability to design and integrate multi-step workflows with external systems and APIs. • Comfortable working from prototype to production with attention to reliability and observability. • Ability to break down ambiguous ideas and propose clear, technically grounded solutions. • Strong communication skills, especially when explaining feasibility and technical trade-offs. • Nice to have: experience with speech-to-text or text-to-speech pipelines. • Nice to have: familiarity with workflow engines or orchestration tools. • Nice to have: experience with evaluation frameworks or custom benchmarking pipelines. • Nice to have: exposure to model monitoring, versioning, or lightweight MLOps practices. • Nice to have: prior involvement in pre-sales or client-facing technical discussions. • Nice to have: experience mentoring engineers on applied AI practices. • Nice to have: experience with no-code tools like n8n.

🏖️ Benefícios

• Become the foundational hire for Applied AI at Logiciel. • Own high-impact projects from design to deployment. • Influence architecture, patterns, and future team growth. • Work in a culture that values engineering clarity, autonomy, and tangible results.

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