AI Solution Developer

Vaga não está no LinkedIn

🕒 Maio 27

🌽 Illinois, New York, +2 estados a mais – Remoto

🌽 Illinois – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🤠 Texas – Remote

💵 $200.000 - $230.000 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

💻 Engenheiro de Soluções

Candidatar-se

Encontrar Vagas Remotas Similares

Receber Emails de Vagas Remotas

📊 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

Jones Lang LaSalle Americas, Inc.

SiteLinkedInTodas as Vagas

10.000+ funcionários

Prestamos serviços imobiliários comerciais para empresas e investidores em todo o mundo, ajudando a reduzir custos, aumentar a produtividade e melhorar a sustentabilidade.

Descrição

• Designing and Building AI Solutions - Your primary output will be AI-powered workflow tools: structured, versioned, documented solutions that guide an AI model through specific PDS tasks. • Scripting and System Integration - Most AI solutions require supporting code: scripts that parse input documents, validate outputs, call external APIs, format deliverables. • AI Solution Design - You will think about what information an AI model needs to do a job well and what it doesn't. • Testing and Quality Engineering - You'll build test harnesses, run solutions against diverse real-world documents, and treat output quality as an engineering problem. • Stakeholder Collaboration - You'll work directly with PDS project managers to understand workflows well enough to build for them accurately.

🎯 Requisitos

• Familiarity with Python: file I/O, data parsing, scripting for automation • Familiarity with REST APIs and enterprise integration patterns • Comfort with version control (Git), structured file management, and markdown-based documentation • Exposure to enterprise security and governance requirements for AI systems (data residency, access controls, audit logging) • Hands-on experience building with large language models: designing system prompts, managing context, structuring inputs and outputs, optimizing for token efficiency, handling failure cases • Understanding of how context design affects LLM output quality and reliability • Familiarity with retrieval-augmented generation (RAG) concepts • Exposure to agentic patterns, tool use, and multi-step AI reasoning • Curiosity about emerging AI infrastructure patterns

🏖️ Benefícios

• 401(k) plan with matching company contributions • Comprehensive Medical, Dental & Vision Care • Paid parental leave at 100% of salary • Paid Time Off and Company Holidays • Early access to earned wages through Daily Pay