Top Artificial Intelligence Jobs in Spain

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

The Senior Product Manager will lead the strategic roadmap, conduct market research, manage monetization strategies, and mentor a team while collaborating across various departments.

Develop and maintain automation testing frameworks, execute tests, and implement AI-driven quality practices while collaborating with product teams.

Design and build the Orion GenAI platform to standardize stacks, accelerate GenAI product delivery, provide reusable components, abstract complexity for application teams, and collaborate cross-functionally to integrate scalable GenAI solutions.

Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.

Teach and mentor experienced engineers transitioning to system architecture roles: run live sessions and office hours, review student projects and architecture, lead architecture review boards, provide detailed technical feedback, record instructional content, and support a team of instructors.

Author and iterate curriculum for experienced engineers transitioning to system architecture: validate syllabus, define learning objectives, produce and fact-check AI-generated lessons, design realistic hands-on projects and rubrics, and collaborate with LX designers to refine content over a 4–6 month, ~15–20 hr/week engagement.

Design and build reusable AI-powered integration and migration components (APIs, connectors, agentic workflows, RAG/GraphRAG), translate product needs into solution designs and implementation increments, define technical guardrails and architecture, evaluate vendor solutions, support PoCs/pilots, and partner with product, cloud, DevOps and country teams to scale secure, observable AI capabilities.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Design, build, and deploy production-grade agentic AI systems at enterprise scale. Lead client engineering engagements, define RAG and multi-LLM standards, own LLMOps and eval harnesses, establish observability, safety, and cost controls, and publish reusable patterns and metrics to demonstrate business impact.

Build and ship AI/LLM-assisted features in Ruby on Rails: integrate LLM APIs, implement retrieval/context patterns, ensure reliability, tests, fallbacks, and production safeguards, and maintain system performance, security, and accuracy.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

As a Staff/Senior AI Engineer, you will develop production-ready coding agents and improve AI model capabilities for JetBrains' software development tools. Collaborating with teams, you'll focus on model training, evaluation, and product integration to enhance user experience.

Support international talent strategy projects across 18 countries: analyze executive talent data, design and pilot AI use cases to optimize HR processes, build KPIs and dashboards, collaborate with senior leaders, and drive implementation and governance for scalable, AI-enabled talent initiatives.

The role involves designing and implementing data models and predictive models in HR using AI, while analyzing large datasets and automating processes.

Design and build autonomous AI agents and multi-agent systems, implement RAG pipelines for IT/AIOps, integrate agents with external systems, add observability and evaluation, and collaborate with product teams to define use cases and quality metrics.

The AI Senior Designer will create and manage high-impact digital marketing campaigns, leveraging AI tools for asset production, and mentoring junior designers.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Design, build, and own production AI products using LLMs, agents and cloud services. Rapidly prototype, deploy, monitor, and iterate AI-powered tools to improve creative workflows, partnering with cross-functional teams and ensuring reliability, security, and maintainability.

Lead an enterprise AI product pod to define use cases, KPIs, scope, and adoption; coordinate cross-functional stakeholders (Legal, Quality, IT/Security); ship LLM/agent/RAG solutions into real workflows; produce reusable Fabric assets; manage roadmap, telemetry-driven adoption, and portfolio reporting.

Build domain data products and pipelines for AI consumption: create retrieval-ready, semantically annotated, contract-governed assets; deploy agents for metadata, entity resolution and classification; ensure data quality, lineage, certification, and reuse; collaborate with domain stewards, business teams and IT to model, store and serve structured and unstructured data for retrieval and modeling.

Lead design, delivery, and continuous improvement of global sales-focused learning programs. Partner with sales leaders to identify capability gaps, implement AI-enabled and digital learning solutions, manage multiple projects, measure learning effectiveness, and coach L&D team members to drive sales performance and business outcomes.

Hands-on lead full-stack engineer who designs scalable architecture, ships production SaaS features, integrates AI tools, mentors senior engineers, improves deployment pipelines, and drives engineering quality.