Lang Chain Deployment Engineer

🕒 Ontem

🇲🇽 México – Remoto

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

⛑ DevOps & Engenheiro de Confiabilidade do Site (SRE)

👻 Score fantasma 12%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.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.

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Nexaminds

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2023

🤖 Inteligência Artificial

💼 Consultoria

🤝 B2B

Artificial Intelligence • Consulting • B2B

A Nexaminds é uma empresa de consultoria em IA e serviços de tecnologia que ajuda negócios a aproveitar a inteligência artificial e o machine learning para aumentar a eficiência, reduzir custos e abrir novas fontes de receita. A empresa oferece serviços de desenvolvimento de AI/ML, engenharia de dados, modernização de aplicações, transformação para a nuvem, desenvolvimento ágil e engenharia de confiabilidade de sites (SRE). A Nexaminds se posiciona como parceira B2B, com estudos de caso em plataformas personalizadas de varejo e assinaturas e na modernização dos setores de logística e transporte, aplicando IA para aprimorar práticas de engenharia, QA e eficiência operacional.

Descrição

• Translate approved LangChain and agentic AI architectures into maintainable, secure, scalable, production-ready applications • Build LLM-powered assistants, copilots, chatbots, document-processing solutions, decision-support tools, and automated workflows • Develop deterministic and agentic workflows using LangGraph, including state management, routing, tool use, retries, checkpoints, memory, streaming, and failure recovery • Design and implement RAG pipelines, including ingestion, chunking, embeddings, indexing, retrieval, reranking, grounding, citations, and access controls • Integrate LLMs, embedding models, APIs, databases, search systems, vector stores, business applications, and custom tools • Deploy AI applications using Docker, Kubernetes, serverless, or managed cloud runtimes • Implement LangSmith tracing, evaluation, monitoring, dashboards, alerts, and feedback workflows • Establish automated quality engineering and evaluation processes • Build offline and online evaluation frameworks, golden datasets, quality thresholds, and release gates • Implement AI security and responsible AI practices • Optimize AI applications for response quality, latency, throughput, reliability, context utilization, caching, model selection, and token consumption • Integrate AI solutions into CI/CD pipelines • Create operational documentation, architecture updates, troubleshooting guides, runbooks, dashboards, alert thresholds, and incident procedures • Partner with AI architects, Nexaminds delivery leaders, and customer stakeholders • Participate in design and code reviews, mentor engineers, and support knowledge transfer • Monitor production systems, analyze feedback and failures, and continuously improve AI solution quality, reliability, performance, and cost

🎯 Requisitos

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent practical experience • Strong professional software engineering experience with Python and/or JavaScript/TypeScript, including API development, asynchronous processing, testing, packaging, dependency management, and code review • Hands-on experience building production-grade AI agents using LangChain and LangGraph, including chains/runnables, tools, structured outputs, stateful workflows, streaming, persistence, and error handling • Experience developing and deploying LLM-powered applications using commercial model APIs or open-source models • Strong understanding of Retrieval-Augmented Generation (RAG), including document ingestion, chunking, embeddings, vector search, metadata filtering, reranking, grounding, and citation patterns • Experience with at least one vector database or search platform • Experience deploying production-grade AI applications using Docker and Kubernetes • Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP) • Experience taking AI applications beyond POCs into production, including CI/CD, environment management, monitoring, troubleshooting, and operational support • Hands-on experience with LangSmith or comparable LLM tracing, evaluation, and observability tools • Familiarity with the Agent Development Life Cycle (ADLC) and production deployment of configurable AI agents • Working knowledge of application security, including IAM, secrets management, encryption, API security, auditability, privacy, and secure software development practices • Strong customer-facing communication, technical writing, and consulting skills • Ability to work effectively in ambiguous environments and collaborate with AI architects, engineering, platform, security, data, and product stakeholders

🏖️ Benefícios

• Stock options • Remote work options • Flexible working hours • Benefits above the law • Mentorship and opportunities to learn and develop professionally • Collaboration with experienced IT professionals

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