Top Artificial Intelligence Jobs in Spain

Lead architecture and hands-on implementation of scalable decision automation and production ML systems for Candy Crush Saga. Build reusable decisioning foundations, improve experimentation and causal inference tooling, mentor senior engineers, and ensure reliability, observability, and cross-team alignment for player-facing personalization and adaptation at scale.

Leads the strategy and implementation of AI-driven continuous auditing, advanced analytics, and algorithmic assurance. Evaluates machine learning models, Big Data platforms, data pipelines, privacy controls, AI governance, and regulatory compliance. Develops automated audit routines, anomaly detection, predictive models, and continuous monitoring frameworks. Partners with data and engineering leadership, provides assurance to executives and the Audit Committee, and enables Internal Audit teams through coaching and new AI-driven tools.

Trainee supporting Data and AI initiatives through data preparation, quality validation, ETL, reporting, AI Assistant maintenance, and machine learning and generative AI testing. The role includes participating in proofs of concept, documenting findings, and collaborating with business, IT, and international stakeholders while learning under experienced mentorship.

Leads enterprise AI adoption programs from assessment through workshops, hands-on enablement, adoption measurement, and team self-sufficiency. Builds client relationships, develops internal AI champions, creates reusable prompts and templates, ensures compliance with data-governance and responsible-AI policies, reports business impact and ROI, and identifies expansion opportunities. The role requires practical AI fluency, strong facilitation, change-management expertise, customer success capabilities, and executive communication skills.

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Own partner onboarding, API integrations, and technical issue resolution for Glovo On-Demand. Identify workflow inefficiencies and implement AI-driven automation to improve scalability, accuracy, and operational capacity. Serve as the bridge between business, partners, Sales, Account Management, and Technology teams while building the AI roadmap and driving structural process improvements.

Lead creative strategy at the intersection of storytelling, digital innovation, data, and business impact. Develop outcome-driven campaigns, leverage AI and automation, support pitches and commercial growth, manage executive client relationships, mentor creative teams, and guide long-term creative roadmaps. Translate complex data and creative concepts into compelling recommendations tied to engagement, ROI, and measurable business results.

Manage and validate sales orders, check product availability, prepare delivery proposals, update order statuses, screen complex orders, coordinate shipments through forwarding, and resolve delivery issues. The intern will also produce monthly reports on orders, deliveries, sales, and stock while applying root-cause analysis and exploring AI and automation to improve operational efficiency and customer experience.

Leads Workday HRIS solution design, configuration, testing, implementation, reporting, security, and support across Core HCM, Talent, and Recruiting. Builds AI-enabled HR automation, ensures data governance and compliance, mentors analysts, manages releases and projects, and advises HR leaders on technology strategy and process improvements.

Build and operate Kraken’s internal AI platform, including governed integrations, authentication, internal tooling, evaluation systems, spend controls, identity and access controls, provisioning, vendor configuration, and policy enforcement. Provide technical enablement through documentation, deep dives, and office hours while supporting teams and administering the enterprise AI estate.

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.