Top Artificial Intelligence Jobs in Spain

Design, deploy, and maintain large-scale HPC/AI clusters including monitoring, storage, networking, virtualization, job schedulers, CI/CD, and automation tooling. Troubleshoot from bare metal through OS and application stacks, document standard methodologies, and support R&D and POCs for optimized AI and GPU computing workflows.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Design and operate AI-ready data products, semantic layers, governed datasets, and natural-language analytics experiences on Snowflake or Databricks. Build ETL/ELT pipelines using dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG and conversational analytics applications; and implement governance, security, lineage, auditability, and performance optimization. Partner with Finance stakeholders to define trusted KPIs, hierarchies, business glossaries, and semantic models in a regulated environment.

Build and ship AI agents that automate insurance brokers’ back-office workflows, including policy reading, claims handling, and insurer correspondence. Develop the platform supporting these agents, own features from concept through production, collaborate with product and business teams, and improve personal automation tooling. The role emphasizes rapid growth, high ownership, pragmatic engineering decisions, and production experience with Node.js and TypeScript.

Design, build, and ship production-grade full-stack software integrated with agentic AI systems and LLM APIs. Use AI-assisted development throughout the software lifecycle, evaluate AI-generated outputs, and manage reliability, latency, token, and cost considerations. Own delivery from design through production support, track AI productivity KPIs, collaborate in Agile client environments, and contribute reusable components, knowledge bases, and AI tooling standards.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Lead Multiverse's earned media and external narrative across the US and Europe. Build in-house PR capability, manage direct media relations, position the CEO and leadership, draft high-quality press materials, translate complex quantum and AI topics for mainstream audiences, coordinate cross-functional alignment, and own crisis communications and coverage reporting.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Build and productionize AI agents for fund-operations workflows, including document extraction, orchestration, evaluation, observability, and secure platform integration. Own architecture, tool contracts, memory, RAG, deployment, provider fallback, cost, latency, and reliability. Establish reusable agent patterns and shared platform capabilities for other engineering teams while mentoring engineers. The role requires end-to-end ownership of live LLM products, measurable evaluation systems, and production backend development using TypeScript and/or Python.

Deliver live workshops and applied labs on AI-assisted visual, video, document, and interactive content creation. Demonstrate generative AI workflows, image and video production, layout tools, vibe-coding, and task automation. Support learners with practical feedback and help them adapt workflows to business environments. Collaborate with curriculum teams to develop templates, exercises, cases, and learning materials, while continuously improving the course based on learner feedback and evolving AI tools.

Build Python-based backend services, APIs, and integrations that embed AI capabilities into a SaaS platform. Develop scalable customer-facing features, connect AI outputs to workflows and dashboards, collaborate with product, design, and platform teams, and implement observability, monitoring, and error handling. Contribute to the AI integration stack, including SDKs, MCP servers, and LLM orchestration.

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

Design and deliver enterprise AI automation using Azure AI Foundry and Python, including document understanding, NLP, agentic workflows, RAG, embeddings, and vector search. Build reusable AI components, secure integrations, evaluation and governance frameworks, and production-ready services with testing, monitoring, observability, and responsible AI safeguards. Collaborate with Payroll and Product stakeholders to identify automation opportunities, translate business needs into technical designs, and guide AI architecture and engineering standards.

Design and build Ebury’s Agentic Data Plane, including agent communication, tool registries, governed data access, security controls, audit logging, observability, and replayability. Integrate AI platform capabilities with data catalogs, metadata, warehouses, and vector retrieval. Collaborate with domain teams to onboard agents and establish platform standards. Explore LLM evaluation, prompt safety, agent memory, and related technologies while operating secure production services on GCP.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Develop scalable backend systems and APIs for AI solutions using Azure services. Build and optimize cloud-based generative AI applications, integrate RAG, speech-to-text, LLMs, and other AI models into production, and potentially implement Intelligent Document Processing solutions. Collaborate with data scientists, AI engineers, and frontend developers while monitoring performance, troubleshooting deployments, and maintaining reliable, secure services. Requires 4–5 years of AI solution development experience and advanced English proficiency.

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Six-month part-time curricular internship for final-year Engineering or IT students completing a Bachelor's final project. The student will support Airbus initiatives involving Industry 4.0, connected systems, machine learning, data analysis, or digital twins. Candidates need B2-level English, analytical and problem-solving abilities, communication skills, teamwork, and the ability to work in an international environment.

Leads the technical strategy and implementation of reusable generative AI document-authoring workflows for pharmaceutical R&D. Develops multi-agent, retrieval-augmented generation, structured-content, evidence-grounding, quality-control, and evaluation capabilities. Defines roadmaps, requirements, data connectivity, platform plugins, and regulatory-readiness practices while guiding engineers, contractors, subject-matter experts, and data science partners from prototypes to scalable enterprise solutions.

Lead the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

Designs and delivers production conversational AI systems, including multi-turn agents, LLM reasoning loops, and multi-agent orchestration. Leads end-to-end AI initiatives using Python, Azure, MLflow, and Databricks; benchmarks and maintains models; mentors junior staff; leads cross-functional teams; and partners with customers to develop solutions for complex enterprise domains.

Build and own AI-powered products and services for Scopely’s creative production teams, from prototyping through deployment, monitoring, and continuous improvement. Develop production-ready applications using LLMs, agents, automation frameworks, and cloud technologies. Collaborate with engineers, designers, artists, producers, and creative technologists to improve workflows, reliability, scalability, and operational efficiency across game teams.