Top Artificial Intelligence Jobs in Barcelona

The Senior Product Manager will lead the Applied AI Learning team to create intelligent, personalized learning experiences by managing the product portfolio and ensuring effective utilization of data for learner progress.

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.

Lead the Data Science and AI teams for recommendation systems and marketing technology, optimizing user acquisition through machine learning initiatives and causal inference.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

Design and ship governed, production-grade LLM systems for enterprise functions (HR, Finance, Legal, Procurement). Lead multi-agent architectures, RAG pipelines, sovereignty-aware routing, governance-as-code, MLOps, resilience engineering, compliance automation, monitoring, and stakeholder adoption to deliver auditable, resilient AI solutions.

Lead design and delivery of governed, production-grade AI for enterprise enabling functions. Build multi-agent LLM systems, RAG pipelines, sovereignty-aware model routing, governance-as-code, MLOps, and resilience controls; partner with legal, security, and business teams to meet multi-jurisdictional compliance and auditability requirements.

Lead strategy and delivery of a multi-cloud AI platform across AWS, Azure and GCP. Define enterprise compute, Terraform foundations, cloud-native AI services, governance, FinOps and compliance (GxP). Build and coach an AI platform/DevOps team, drive adoption, partner with R&D, security and compliance, and own budgets and vendor relationships.

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.

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.

Lead adoption of AI-driven QA by evaluating and building AI-assisted quality checks, integrating them into CI workflows, defining quality gates, running experiments, translating QA knowledge into scalable playbooks, and partnering with cross-functional teams to improve release risk assessment and product quality.

Lead strategy, roadmap and delivery for enterprise AI products and platforms. Own vision, investment priorities, governance, budgets and benefits. Manage complex, cross-functional delivery programs, risk, compliance (including GxP), and vendor relationships. Build and coach product/delivery teams, define success metrics, and ensure secure, scalable, auditable AI capabilities adopted across the enterprise.

Senior leader owning vision, strategy and roadmap for enterprise AI products and platforms. Leads complex delivery programmes, governance, budgeting, benefits tracking and cross-functional teams to deliver compliant, secure, scalable AI capabilities and adoption across the organisation.

Support AI and Data Consulting teams to analyze pharma manufacturing data, train and fine-tune ML models, identify ETL bottlenecks, prototype and productionize AI solutions for a SaaS platform, and validate algorithms to meet pharmaceutical quality standards.

The Product Manager will drive the growth of an AI-powered business writing assistant through product strategy, development, and performance optimization, collaborating with cross-functional teams.

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.

The Senior Product Development AI Engineer will design and develop backend services with AI capabilities, collaborate with teams, and contribute to scalable system architecture.

Partner with business functions to drive AI adoption: map and prioritise use cases, build quick automation or route engineering work, coach AI Champions, co-own upskilling and governance, and scale cross-functional AI programmes to measurable adoption and ROI.

Lead and build Maze’s new AI-native Market Research service: define research vision and methodologies, recruit and manage a research team, own delivery quality, partner with Product/Engineering and GTM, and serve as executive research lead on strategic customer engagements.