AI Engineer

Wizeline·Barcelona - Spainsenior

En corto

→Construyes herramientas de IA empresariales con agentes y LLMs en producción.
→Desarrollas sistemas completos (backend + frontend) desde prototipo hasta producción sin dependencias externas.
→Destacan las herramientas de evaluación de prompts y mantenimiento de calidad en escala.

Proficiency in English required for collaboration with global teams.

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¿Qué piden?

✓Licenciatura o maestría en CS, Ingeniería o campo STEM relacionado.
✓4+ años de experiencia en ingeniería de IA o ML.
✓Dominio de Python y/o TypeScript/Node.js.
✓Experiencia en despliegue de LLMs en producción.
✓Conocimiento práctico de LangChain y sus ecosistemas (LangGraph, LangSmith).
✓Experiencia con RAG y bases de datos vectoriales en producción.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonTypeScriptNode.jsReactVueNext.jsAWSGCPLangChainLangGraph

¿A quién escribirle en Wizeline?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

We are: Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact. With the right people and the right ideas, there's no limit to what we can achieve. Are you a fit? Sounds awesome, right? Now, let's make sure you're a good fit for the role: Key Responsibilities • Architect and ship end-to-end agentic and LLM-powered tools for business-facing use cases, deciding when to use a single LLM call, an iterative LLM loop, or a full multi-agent system based on real task complexity. • Design AI-agnostic, model-flexible services that allow the team to evaluate and swap the best-performing model for each task. • Build production tools that transform raw content into structured, ready-to-use output — for example, systems that reformat content to defined templates/guidelines or consolidate multiple sources into a single, fact-accurate output without inventing information. • Develop and maintain RAG pipelines and vector database integrations to support retrieval-driven features such as content linking and recommendations. • Establish and scale prompt evaluation, testing, and regression-control frameworks (e.g., via Braintrust, MCP tooling, LangFuse) so quality holds as tools expand across teams and use cases. • Take AI features from prototype/PoC through to deployed, end-user-facing production tools, working across the full stack (AI core services in Python/TypeScript, front-end integration in React/Vue/Next.js) without relying on handoffs to other teams. • Partner with stakeholders and engineering leadership to gather feedback, measure impact (e.g., time saved, approval rates), and iterate on tools in production. • Extend proven architectures to onboard new use cases as configuration rather than one-off rebuilds. • Stay current on GenAI, NLP, ML, and IR technologies, incorporating best practices and cloud infrastructure to improve system efficiency. Must-have Skills • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field, or equivalent work experience. • 4+ years of industry experience in machine learning engineering, AI engineering, or a related software engineering role. • Strong programming skills in Python and/or TypeScript/Node.js, with the ability to build both AI core services and the interfaces that consume them. • Hands-on experience deploying LLMs in production, building automated evaluation pipelines (e.g., LLM-as-a-judge), and architecting multi-agent systems that use tool-calling and long-term memory to solve non-linear problems. • Practical experience with LangChain and its ecosystem (e.g., LangGraph, LangSmith) or comparable agent-orchestration frameworks. • Experience with RAG architectures and vector databases in production settings. • Full-stack capability (front-end frameworks such as React/Vue plus back-end services on cloud infrastructure such as AWS/GCP) sufficient to ship complete features independently. Nice-to-have • Experience with Vertex AI or equivalent multi-model cloud AI platforms. • Familiarity with prompt-management and observability tooling such as Braintrust, LangFuse, or MCP-based systems. • AI Tooling Proficiency: comfort using AI tools to optimize day-to-day work (drafting, analysis, research, automation), with the ability to recommend effective AI use and identify workflow improvements for the team. • Familiarity with Docker and Git version control.

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