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AI Platform Engineer, Harness

🕒 Julho 29

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🏗️ Engenheiro de Plataforma

🗣️🇺🇸🇬🇧 Inglês obrigatório

De onde vem esta vaga

Publicada na página de carreiras da LTS, não em um site de vagas

Vista pela primeira vez pelo Remote Rocketship Julho 29

Ainda aberta no site da LTS. Verificada 5 horas atrás.

Logo of LTS

LTS

1001 - 5000 funcionários

🏥 Saúde

💼 Consultoria

📦 Logística

Healthcare • Consulting • Logistics

A LTS é uma empresa voltada para apoio a missões governamentais e serviços de saúde, oferecendo resposta a emergências, saúde ocupacional, soluções automatizadas de saúde e consultoria corporativa. Ela proporciona rápida mobilização em todo o país para resposta a desastres, logística e assistência em massa, além de oferecer quiosques de diagnóstico automatizado, testes laboratoriais, pessoal médico, clínicas no local e gestão de testes/vacinação para populações carentes e remotas. A LTS também oferece modernização de TI, integração de nuvem e sistemas, cibersegurança e gestão de riscos, IA/ML e análise de dados, e gestão de programas para clientes federais, estaduais e locais, com forte conformidade (padrões HIPAA e federais) e experiência em aquisições no setor público.

Descrição

• Design, build, and maintain enterprise AI platform capabilities supporting Large Language Models (LLMs), AI agents, RAG, and Generative AI applications. • Develop reusable AI harnesses to automate testing, prompt evaluation, model benchmarking, regression testing, and quality assurance. • Build AI evaluation frameworks to measure model accuracy, retrieval quality, hallucination detection, latency, throughput, cost, and overall application performance. • Implement observability and monitoring solutions for AI applications, including telemetry, tracing, logging, dashboards, and operational metrics. • Build and maintain LLMOps pipelines supporting model deployment, versioning, evaluation, experimentation, rollback, and continuous improvement. • Design automated workflows for prompt testing, retrieval evaluation, AI system validation, and performance benchmarking. • Develop internal tools for prompt management, model experimentation, AI performance optimization, and developer productivity. • Build scalable backend services and APIs supporting AI platforms and enterprise AI integrations. • Collaborate with AI architects and engineering teams to integrate LLMs, RAG pipelines, vector databases, and agentic AI solutions into enterprise applications. • Support deployment of AI services across AWS, Azure, or Google Cloud using containerized and cloud-native architectures. • Implement CI/CD pipelines and infrastructure automation supporting enterprise AI development and deployment. • Apply security, governance, and Responsible AI controls throughout the AI development lifecycle. • Evaluate emerging AI frameworks, LLMOps technologies, evaluation methodologies, and automation tools to improve engineering productivity. • Troubleshoot production AI issues and continuously improve platform reliability, scalability, security, and user experience. • Document engineering standards, AI platform architecture, evaluation methodologies, and operational best practices.

🎯 Requisitos

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field. • 5+ years of experience in software engineering, platform engineering, backend engineering, DevOps, cloud engineering, or infrastructure engineering. • 2+ years building or supporting Generative AI, Large Language Model (LLM), or machine learning applications. • Strong programming experience in Python. • Experience developing APIs, backend services, and distributed systems. • Experience with cloud platforms including AWS, Azure, or Google Cloud Platform. • Experience deploying applications using Docker and Kubernetes. • Experience working with Git, CI/CD pipelines, Infrastructure as Code (IaC), and infrastructure automation. • Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt engineering, Embeddings, Vector databases, AI agents and agentic workflows • Familiarity with AI evaluation techniques, automated testing, benchmarking, regression testing, and model validation. • Experience building scalable, production-grade software platforms. • Strong problem-solving, debugging, and performance optimization skills.

🏖️ Benefícios

• The Opportunity to support high-visibility federal missions • A culture that values innovation, growth, and collaboration • Access to cutting-edge tools and technologies • Comprehensive benefits for you and your family • A career path that rewards ambition and performance

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Desenvolvido por Lior Neu-ner. Adoraria receber seu feedback — entre em contato por DM ou pelo e-mail support@remoterocketship.com
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