• Diseñar arquitecturas end-to-end para agentes conversacionales y flujos GenAI. • Implementar pipelines de embeddings, retrieval y orquestación con RAG. • Desarrollar soluciones basadas en LLMs (GPT, Claude, Llama, etc.) usando frameworks como LangChain o LlamaIndex. • Integrar soluciones de IA con APIs y microservicios existentes. • Aplicar fine-tuning, prompt engineering y estrategias de optimización de modelos. • Evaluar calidad de respuestas usando frameworks como RAGAS, LangSmith o PromptFlow. • Monitorear y mejorar experiencia de usuario con agentes de IA en producción. • Documentar, versionar y mantener workflows para proyectos de IA de alto impacto.

We’re looking for an Applied AI Engineer to join our Engineering team to design, build, evaluate, and deploy intelligent AI systems that power enterprise workflows, autonomous assistants, and machine-assisted task automation. This role is based out of our US office and comes with competitive pay and practical benefits for you and your family. We’re looking for strong problem solvers who’ve shipped impactful AI-driven functionality before and can scale our platform intelligence with quality and ownership. What you'll do — responsibilities Design, build, and fine-tune transformer-based and retrieval-augmented models for natural language understanding, intent classification, summarization, and context-aware reasoning. Develop AI components that power automated knowledge search, semantic retrieval, agentic task orchestration, and conversational assistants. Integrate AI systems with production data pipelines in a scalable and secure way. Build evaluation pipelines, define metrics, and establish performance and reliability benchmarks for AI features. Collaborate with engineering, data, and product teams to productize models with observability, monitoring, and continuous improvement. Participate in code reviews, architecture discussions, and cross-team problem solving. Take end-to-end ownership of features — from problem definition to deployment and performance monitoring. What we’re looking for (qualifications) Take full responsibility for wins and setbacks. Mastery: Commit to continuous learning and improvement. Impatience: Move with urgency towards outcomes. Customer Obsession: Put customer value at the heart of every decision.

• Support the architecture, design, and deployment of scalable AI/ML systems under the guidance of senior engineers. • Designing, testing and optimizing prompts for LLMs to improve output quality and task accuracy. • Collaborate with cross-functional teams, including software engineers and data scientists, to integrate AI/ML technologies into existing frameworks. • Assist in developing end-to-end AI/ML solutions, including data processing and deployment. • Apply AI to real world problems – data preprocessing, analytics, and insights generation. • Participate in requirements gathering, system design reviews, and technical documentation. • Help develop communications and training packages about AI/ML. • Stay current with industry trends and emerging technologies in AI/ML.

• Design, develop, and enhance applications based on foundation models (e.g., LLMs, multimodal models) tailored to specific business needs; • Implement and experiment with generative AI techniques, including but not limited to Retrieval-Augmented Generation (RAG) and prompt engineering; • Lead the implementation of intelligent autonomous agents and multi-agent systems capable of complex decision-making, task execution, and interaction with external systems; • Use and contribute to agentic AI frameworks (e.g., LangChain, AutoGen, Semantic Kernel, Crew AI) to build robust and scalable agents; • Develop and integrate Model Context Protocol (MCP) solutions to standardize how AI applications access and use external data sources, tools, and real-time information; • Lead the development of full-stack applications that integrate generative AI models and agentic systems, ensuring seamless user experiences; • Work with front-end technologies (e.g., React, Angular, Vue.js, JavaScript, TypeScript, HTML, CSS) and back-end technologies (e.g., Python with Flask/Django/FastAPI, Node.js, Go); • Design and implement scalable RESTful APIs and microservices to expose AI functionality; • Deploy, manage, and optimize AI/ML workloads and full-stack applications on at least one major cloud platform (AWS, Azure, or GCP); • Implement LLMOps/MLOps and DevOps practices for continuous integration, continuous delivery (CI/CD), model deployment, monitoring, and lifecycle management; • Stay up to date with the latest advances in generative AI, agentic systems, foundation models, and related areas through ongoing research and experimentation; • Collaborate effectively with cross-functional teams, including product managers, data scientists, researchers, and other engineers; • Mentor junior engineers, share knowledge, and foster a culture of technical excellence.