Top Artificial Intelligence Jobs in Barcelona

The VP Field Marketing is responsible for leading marketing strategy and execution in Europe, focusing on demand generation, brand awareness, and pipeline development while collaborating cross-functionally and driving a data-driven culture.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

Drive development and deployment of AI solutions, translating business needs into product requirements, managing backlogs, and coordinating cross-functional teams.

The Senior Data & AI Strategist will lead data and AI transformation initiatives, advise clients, and ensure impactful strategy delivery, while mentoring team members.

The Project Manager will lead software integration projects in the life sciences sector, managing resources, tracking progress, and ensuring timely delivery while collaborating with various stakeholders to improve platform solutions.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

Define and enforce enterprise architecture for Migrant Solutions. Design and implement software landscapes, APIs and web-based applications. Create roadmaps, plans, POCs, and technical guidance. Track risks and ensure solutions align with organizational requirements and enterprise architecture.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

The Chief Data & AI Officer will lead the establishment of a company's data and AI infrastructure, ensuring reliable information capture, decision-making processes, AI system integration, and compliance with governance standards.

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.

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.

Lead a cross-functional squad to define product vision, roadmap and deliver new features for institutional English assessments. Translate business needs into requirements, write and prioritize epics and user stories, run agile ceremonies, collaborate with UX/development/marketing/operations, use data to iterate, and mentor colleagues to achieve measurable business outcomes.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Support central B2B marketing by using generative AI to create and repurpose content, draft social copy and visual prompts for LinkedIn and Meta, collaborate on creatives, and monitor performance to optimize campaigns.

Lead AI adoption across Wind Engineering by designing adoption plans, training programs, communities of practice, communications, and change management. Own AI enterprise cost budget, track adoption metrics, govern tool investments, and drive continuous improvement to embed AI into engineering workflows for measurable value.

Define and maintain enterprise AI reference architectures, reusable solution patterns, and MLOps/LLMOps standards. Guide subsystem designs for scalability and reuse, update engineering standard work and QMS for AI, partner with Digital/IT on platforms and integrations, mentor Embedded AI Architects, and serve as technical escalation for high-risk AI initiatives across Wind Engineering.

Lead Lesson Insights product end-to-end: strategy, roadmap, and delivery of AI/LLM-driven feedback across web and mobile. Prioritize actionable feedback, connect per-lesson insights to long-term progress, involve tutors, scale to languages, drive engagement and reach, and perform hands-on data analysis and experimentation to measure impact.

Own a portfolio of AI agent deployments in healthcare, acting as GM for performance, commercial success, and ARR. Drive product, operations, analytics, and engineering improvements, lead teams, build playbooks, and manage executive client relationships to scale deployments.

As an AI Director, lead AI and data programs for Specialty Chemicals, drive account growth, mentor teams, and engage C-level stakeholders strategically.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will innovate model compression techniques for multimodal AI systems, focusing on quantization, knowledge distillation, and pruning for efficient deployment on edge devices.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will optimize model serving and inference architectures, focusing on high-performance, resource-efficient solutions for AI systems across various deployment environments.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.