Job Summary: The Artificial Intelligence Engineer leads the development and operationalization of intelligent solutions based on large language models, building RAG pipelines and architecting multi-agent systems. Key Highlights: 1. Lead the development and operationalization of AI solutions 2. Create and maintain intelligent workflows with N8N and business automations 3. Work with LLMs, fine-tuning, and prompt engineering **The Mission** The Artificial Intelligence Engineer is responsible for leading the development and operationalization of intelligent solutions built upon large language models. This person will build RAG pipelines, design agent and multi-agent systems, and ensure efficient integration of models into company processes. **Your Challenges Will Include** * Developing RAG (Retrieval-Augmented Generation) pipelines to integrate LLMs with internal knowledge bases. * Creating and maintaining intelligent workflows with N8N, integrating APIs, AI agents, and business automations. * Designing and implementing agent and multi-agent architectures, focusing on model collaboration and task orchestration. * Working with large language models (LLMs), performing fine-tuning, prompt engineering, and performance evaluation. * Integrating generative models into internal systems while ensuring scalability, security, and governance. * Collaborating with data, product, and technology teams to transform business requirements into AI-powered solutions. * Monitoring and optimizing production models, adjusting metrics related to cost, latency, bias, and robustness. **First and foremost, you must align with our purpose: valuing people so each one can build their own story.** **In addition, you must have** * A degree in Computer Science, Engineering, Mathematics, or related fields. * Experience with large language models (e.g., GPT, Gemini, Mistral, LLaMA). * Knowledge of RAG, vector databases, and prompt engineering. * Understanding of machine learning techniques, including classification, regression, clustering, and other algorithms. * Hands-on experience with N8N or similar automation and orchestration tools. * Experience with AI infrastructure: Docker, Kubernetes, MLFlow, Airflow, etc. * Experience handling large volumes of structured and unstructured data using SQL databases, Spark, and Athena. * Advanced Python programming skills, utilizing frameworks such as HuggingFace, LangChain, and Transformers. * Familiarity with cloud computing platforms (AWS, Azure) and MLOps best practices. * Ability to work with unstructured data (text, images, audio). * Awareness of AI ethics and governance, including security, privacy, and bias mitigation.