AI Engineer

• Lead the adoption and evolution of agentic development tools (with Claude Code as the primary tool) within multidisciplinary squads.
• Define and disseminate AI-assisted development methodologies — based on Spec-Driven Development (Spec-Kit, BMAD) — adapting the lifecycle to each product’s context.
• Design and implement agentic processes across the engineering workflow: AI-assisted code review, generation and maintenance of tests, assisted refactoring, living documentation, and PR automation.
• Mentor peers with support, raising the team’s maturity in AI-assisted engineering.
• Support the squad’s backlog organization, prioritizing technical debt, architectural risks, and AI automation opportunities.

Mid level/Senior, AI Engineer

• Você vai construir a plataforma de agentes de IA da Guanabara e o ecossistema que distribui IA generativa governada à companhia.
• Orquestração de agentes — construir agentes com LangGraph no padrão Supervisor/ReAct, com memória de curto/longo prazo e estado durável (PostgresSaver).
• Camada semântica e RAG — evoluir a camada semântica determinística (Cube.js) e a recuperação semântica com Qdrant (busca vetorial + re-ranking) que ancoram as respostas em dado real.
• Skills, subagentes e MCP — criar e versionar skills (SKILL.md), subagentes e ferramentas MCP (FastMCP) para o Claude Code, em fluxo spec-driven, conectando agentes a sistemas corporativos.
• Verificação e avaliação — manter o verificador (LLM-as-judge) e suítes de eval (offline/online) com amostragem de traces para pegar drift de qualidade.
• Plataforma de modelos — operar o gateway LiteLLM (Virtual Keys, budget, RBAC, roteamento multi-provider) otimizando custo e latência.
• Adaptação e serving de modelos — quando RAG e prompting não bastam, fazer fine-tuning supervisionado e PEFT (LoRA/QLoRA), quantização e destilação; servir modelos abertos/privados com vLLM e roteamento SLM↔LLM.
• Governança e segurança — aplicar guardrails de PII (Presidio), defesa contra prompt injection e observabilidade ponta a ponta (Langfuse).

Senior AI Engineer – Agentes, Plataforma & RAG

• Ajudar grandes empresas a transformar o potencial da AI em impacto real nos negócios com AI Deployment, execução AI-native e tech-integrated business solutions.
• Colaborar para construir soluções com impacto real.
• Manter controle de qualidade e operar em produção com segurança.
• Estruturar especificações que alimentam a IA.

Senior AI Developer

• Desenvolver protótipos e MVPs com IA generativa, automações e integrações.
• Criar fluxos automatizados utilizando n8n e integrações via APIs.
• Atuar com ferramentas como Claude, Claude Code, Lovable, OpenAI e similares.
• Desenvolver ou evoluir soluções usando duas ou mais linguagens (Node.js, React, PHP e Python).
• Integrar sistemas internos, bancos de dados, aplicações web e ferramentas de IA.
• Apoiar desenho técnico de agentes, copilots, assistentes e fluxos inteligentes.
• Avaliar viabilidade técnica, esforço, riscos e arquitetura das soluções.
• Apoiar boas práticas de segurança, logs, autenticação e uso seguro de dados.
• Documentar soluções e apoiar a evolução de MVPs para produção.
• Atuar consultivamente no mapeamento de processos As-is e proposição do To-Be.

Senior AI Engineer

• Projetar, implementar e escalar soluções baseadas em Modelos de Linguagem de Larga Escala (LLMs);
• Construção de APIs: Desenvolver e manter microsserviços robustos em Python e Java;
• Orquestração e Integração: Configurar e gerenciar o fluxo de dados entre o backend e API Gateways;
• Ciclo de Vida de IA (AIOps): Implementar práticas de AIOps para monitoramento de modelos em produção;
• Desenvolvimento de Agentes: Projetar e implementar agentes de IA autônomos;
• Otimização de LLMs: Aplicar técnicas de Prompt Engineering e fine-tuning;
• Arquitetura de Dados: Garantir que as integrações de backend suportem o processamento de dados em tempo real.

• Design, implement, and optimize the data infrastructure that supports AI and machine learning (ML) solutions
• Ensure data availability, quality, and security
• Ingest and transform data for the data lake and Feature Store
• Develop and support the operationalization of AI and ML models
• Conduct complex analyses and monitor data quality

Senior AI Engineer, ADK GCP

• Provide technical leadership to more junior members of the Applied AI Engineering team;
• Identify opportunities to apply AI to drive business impact;
• Perform exploratory data analysis and data visualization to understand business needs and inform LLM applications;
• Develop patterns and practices to improve the scalability of LLM applications;
• Optimize the Applied AI Engineering development stack (CI/CD, evaluation of libraries, etc.);
• Develop AI applications using vector databases, LLMs, single-agent and multi-agent architectures;
• Build validation pipelines for LLM applications, including datasets and using LLMs as a judge;
• Develop small back-end and front-end components to validate proofs of concept for LLM applications;
• Actively research technical blogs, academic papers and video conferences to generate improvement and optimization hypotheses for LLM applications;

• Work on development, automation and artificial intelligence, using automation and AI tools to achieve operational efficiency gains, reduce manual tasks, and continuously improve information flows.
• Create process automations using tools such as n8n, UiPath, Orchestrator or similar.
• Integrate different data sources (APIs, relational databases, files, internal and external systems).
• Apply Data Science and AI techniques to optimize processes and generate insights.
• Support the implementation of applied AI solutions (classification, prediction, cognitive automation).
• Ensure the quality, consistency and traceability of the data used in automated processes.
• Document flows, business rules and automations developed.
• Collaborate with business, engineering and analytics teams to understand requirements.
• Participate in POCs with vendors and new automation and AI technologies.

• Be responsible for the squad's technical delivery, ensuring quality, predictability and adherence to architectural standards
• Lead technical refinement with the product team, translating business needs into viable, well-sized solutions
• Support the squad backlog organization, prioritizing technical debt, architectural risks and AI automation opportunities
• Lead the adoption and evolution of agent-based development tools (Claude Code in particular) in multidisciplinary squads
• Define and disseminate AI-assisted development methods — using Spec-Driven Development (Spec-Kit, BMAD) as a foundation — adapting the lifecycle to each product context
• Design and implement agent-driven processes across the engineering workflow: AI-assisted code review, test generation and maintenance, assisted refactoring, living documentation, PR automation
• Act as a systems architect with an AI perspective: evaluate trade-offs, propose integration patterns between agents and services, ensure consistency across iOS, Android and BFF
• Mentor engineers, raising the team's maturity in AI-assisted engineering

AI Engineer – Software Architect

• Operate the SDLC natively with AI
• Maintain quality control and operate securely in production
• Structure specifications that feed the AI
• Interact with diverse tools and environments

AI Developer – PL/SR

• Operate the Software Development Life Cycle (SDLC) natively with AI
• Consistent use of automations with agents that perform multi-step tasks
• Structure specifications that feed the AI
• Maintain quality control and operate securely in production

AI Developer – Mid/Senior

• Spearhead the development of complex, scalable systems while navigating intricate stakeholder landscapes.
• Define the technological vision for mission-critical products, balancing technical debt with business velocity and ensuring the architecture supports both business growth and the diplomatic alignment required in high-stakes client environments.
• Architecture & Stakeholder Leadership: Design and oversee distributed systems architectures with a focus on resilience, low latency, and global scalability.
• Serve as the strategic bridge between client demands and technical feasibility, ensuring engineering efforts translate into immediate business value.
• Strategic Client Management: Apply the political savvy necessary to manage expectations across diverse partners, converting vague requirements or organizational pressures into structured, deliverable technical roadmaps.
• Product Vision & Analysis: Translate complex financial models and business needs into elegant technical solutions, building systems that support real-time data processing and high-complexity analysis for enterprise decision-making.
• Automation & Resilience: Implement and optimize CI/CD pipelines and infrastructure automation, delivering the agility clients demand with the security and stability required in Linux/AWS environments.
• Diplomatic Mentorship: Raise the team’s technical bar beyond code—coaching engineers on how to present technical trade-offs persuasively to non-technical business leaders.

Master AI Developer

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Arquiteto de IA

🕒 June 7, 2025

Keyrus

1001 - 5000

🤝 B2B

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As an AI Architect at Keyrus, design architectures for generative AI solutions and lead multidisciplinary teams.

🇧🇷 Brazil – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 AI Engineer

🗣️🇧🇷🇵🇹 Portuguese Required

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Staff AI Engineer

• Develop, train and optimize AI and Machine Learning models;
• Structure data pipelines for model training and inference;
• Implement AI solutions in production and scalable environments;
• Work closely with product, engineering, data and business teams;
• Evaluate performance, accuracy and efficiency of developed models;
• Research and apply new technologies, frameworks and AI best practices;
• Ensure governance, security and quality of implemented solutions;
• Support intelligent automation initiatives and the use of generative AI.

• Translate business needs into structured specs — the starting point for any delivery
• Select and trigger the correct Skills for each task from the repository maintained by Forge
• Guide AI agents in Antigravity or similar tools to execute complex tasks
• Validate outputs — functionality, adherence to standards, and expected business result
• Report gaps and unexpected behaviors to the AI Architects team
• Maintain delivery cadence — you are responsible for the product, not the code
• Collaborate with business stakeholders to refine specs and ensure alignment before execution

• Lead AI initiatives technically within Suno
• Design architectures using LLMs, agents, and intelligent automations
• Integrate AI with internal systems and products
• Build intelligent data pipelines
• Automate relevant operational processes
• Rapidly prototype solutions (hands-on)
• Define standards and best practices for AI usage
• Serve as a technical point of reference for the team

Artificial Intelligence Engineer – Senior

• Desenvolvimento, implementação e otimização de soluções de inteligência artificial
• Aplicação para melhorar processos, eficiência operacional e tomada de decisão
• Atuação em conjunto com equipes de tecnologia
• Garantir a qualidade e evolução dos modelos e dados
• Monitorar desempenho, documentar soluções e acompanhar tendências do mercado

Engenheiro de Inteligência Artificial, Pleno

• Conversational Agent Development: Build and evolve AI agents for customer service, negotiation, and conversion across channels such as WhatsApp, voice, and webchat.
• LLM Orchestration: Implement flows using frameworks like LangGraph (or similar), managing context, memory, tools (tool calling), and conversation states.
• Prompt Engineering: Create, test, and optimize prompts for different scenarios (collections, sales, support), ensuring consistency and performance.
• Tool Integration: Connect agents to APIs and external systems (e.g., Pix payment generation, data lookup, CRM), enabling in-conversation actions.
• AI Quality Control: Monitor responses, prevent hallucinations, and ensure adherence to business rules.
• Personalization: Work with contextual data (customer profile, history, segmentation) to make interactions smarter and more effective.
• Experimentation: Run A/B tests on approaches, flows, and prompts, continuously improving agent performance.
• AI Observability: Track usage, cost, latency, and response quality metrics (LLM monitoring).
• Cost Optimization: Implement strategies to reduce cost per interaction (model selection, caching, fallbacks, etc.).
• Collaboration: Work closely with backend, product, and operations teams to ensure AI delivers real business impact.

• Instrument agents with trace-level observability (Langfuse or equivalent)
• Build evaluation datasets from production traffic
• Design and operate scoring pipelines (quality + robustness scoring)
• Stand up and operate Braintrust or equivalent evaluation framework
• Build feedback loops from eval → prompt → architecture → re-evaluation
• Implement DSPy / DPO optimization as system matures
• Partner with technical leadership on reliability-sensitive architecture decisions
• Own dev → test → deploy → on-call sustain for your workstream

Applied AI Engineer – Agent Reliability

• Develop and implement AI solutions using frameworks such as LangChain, LangGraph, and multi-agent systems.
• Utilize prompt engineering and retrieval-augmented generation (RAG) to enhance AI capabilities.
• Work with vector databases (ChromaDB, Pinecone) and implement context optimization and model routing.
• Ensure LLM observability and adherence to AI governance standards.
• Lead Agile teams through project lifecycles from conception to deployment, ensuring best practices in software development and architecture.
• Manage CI/CD processes, including logging, tracing, and monitoring for infrastructure observability.
• Conduct code reviews and enforce design patterns to maintain high-quality code standards.
• Collaborate effectively with stakeholders to align on project goals and deliverables.

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