• Lead AI/ML innovations - from coming up with ideas for research, POCs, to contributing to full product feature build out. • Act as GenAI expert - live and breathe AI field news and trends. • Hands-on work trying out new concepts, AI frameworks and tooling around them for different use cases - context management, memory, retrieval, agentic loop, A2A and many other solutions. • Be part of product development - from discovery together with PMs to delivery within cross-functional teams. • Evaluate new LLMs (proprietary and OSS), and assess for performance, cost, and other metrics. • Bonus: act as AI evangelist to promote the adoption and understanding of AI both within and outside nexos.ai.

• Liderar o time de desenvolvimento de IA • Orquestrar a comunicação entre stakeholders e time de executores • Gerenciar backlog de produto e priorização estratégica • Promover um ambiente de alta performance • Atuar na intervenção de impedimentos e fomentar feedback contínuo • Colaborar com diferentes agentes autônomos

• Design and build RAG and automation workflows inside our VPC environment. • Create integrations across engineering, communication, and knowledge-management systems (Jira, Confluence, Slack, Google Drive, Git). • Develop AI-powered pipelines for code quality checks, ticket auto-classification, documentation updates, release notes, and meeting summaries. • Work on prompt engineering, model selection and routing (Haiku / Sonnet / Opus), and workflow optimization. • Build evaluation and regression frameworks for AI workflows to prevent model-upgrade regressions. • Create lightweight dashboards for engineering leads to surface delivery patterns and bottlenecks. • Own initiatives end-to-end: scope → design → ship → measure → iterate.

• Design, develop, and deploy AI/ML models for production environments. • Build and optimize AI-powered applications using large language models (LLMs) and generative AI tools. • Develop intelligent automation workflows, chatbots, recommendation systems, and predictive analytics solutions. • Fine-tune, evaluate, and improve machine learning models for performance and scalability. • Collaborate with software engineers and product teams to integrate AI features into existing platforms. • Work with structured and unstructured datasets for training and inference pipelines. • Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems. • Monitor AI systems, troubleshoot issues, and continuously improve model accuracy and efficiency. • Stay updated with the latest advancements in AI, machine learning, and automation technologies. • Maintain documentation for AI models, systems, and workflows.