Top Artificial Intelligence Jobs in Barcelona

As an AI Product Manager, you'll shape the AI product roadmap, collaborate with cross-functional teams, and ensure AI technologies meet user needs and business objectives.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams to enhance user experiences, and implement AI techniques to improve app performance.

Seeking a Full-Stack Software Engineer to build the core of an Agentic AI Platform. Responsibilities include frontend and backend development, product design collaboration, and infrastructure management with a focus on clean, maintainable code.

Join the AI Engineering team to design, build, deploy, and scale production AI systems. Translate business problems into AI solutions, industrialize ML models, design AI pipelines and platforms, ensure performance and monitoring, and drive MLOps best practices across architecture and governance.

Lead design and delivery of multi-cloud AI compute services, drive capacity planning, GPU orchestration and cost/sustainability optimisation, embed MLOps and compliant-by-design practices, define platform roadmaps/SLAs, mentor engineers, and collaborate with data science, security, and governance teams to enable scalable AI workloads across enterprise environments.

Support design, implementation and maintenance of data ingestion, ETL/ELT and enrichment pipelines for knowledge-graph and LLM applications. Perform data cleansing, normalization, Cypher and SQL querying, basic data-quality checks, ontology-informed KG enrichment, and assist deployment and integration of AI/ML solutions. Collaborate with data scientists, domain experts and stakeholders, document models and workflows, and provide demos and support.

Support design, build and maintain data ingestion, cleansing, enrichment and ETL/ELT pipelines for knowledge graph projects. Write/optimize SQL and Cypher, apply basic KG techniques and ontologies, integrate KG with LLM-powered applications, run data quality checks, monitor pipelines, document models/workflows, and support stakeholders and AI/ML deployments.

Lead design and delivery of multi-cloud, compliant AI compute services. Own capacity planning, GPU/HPC provisioning, cost and sustainability optimisation, MLOps standardisation, productised platform roadmaps and SLAs, operational simplification, technical leadership/mentoring, and collaboration with security, compliance, and ML teams to support enterprise AI workloads.

Design, develop, deploy, and maintain scalable AI/ML systems and pipelines. Apply MLOps/LLMOps/AgentOps best practices, run large-scale experiments, build engineering infrastructure for batch and real-time workflows, and integrate models into business applications with monitoring and APIs. Collaborate with scientists, engineers, and product teams to deliver end-to-end solutions using Python, MLflow, Azure, and Databricks.

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.

Lead end-to-end delivery of production AI products using LLMs, agents and cloud technologies. Prototype, deploy, monitor, and scale AI-powered tools for creative teams, collaborate cross-functionally, drive architecture and engineering standards, and ensure reliability, observability, security and maintainability.

Senior hands-on SRE who designs and runs observability, automation, and AI-augmented operations for AI/ML platforms on Azure/AWS. Leads incident response, instrumentation, runbook automation, platform onboarding, mentoring, and continuous improvement.

Senior hands-on SRE responsible for designing and running observability, automation, and AI-augmented operations across Azure/AWS platforms. Lead incident response, implement telemetry and runbooks, drive continuous improvement, mentor L1/L2 engineers, and support operational readiness and platform onboarding.

Design and build LLM-powered tools, agents, and automations to streamline internal processes across teams. Scope, implement, integrate, and ship agents and automations (LLMs, RAG, tool use) with measurable productivity metrics, integrating with systems like Slack, Ashby, and BigQuery.

Design and implement production-grade AI services and agentic orchestration using LLMs and vector search. Build Python-based APIs, shared libraries, and retrieval systems; ensure observability, scalability, and reliability while collaborating across product and platform teams.

Build and maintain production-grade backend services that integrate LLMs (Claude/OpenAI/open-source) into compliance workflows, automating AML/AFC processes, owning architecture, reliability, monitoring, and driving product and technical decisions within a small engineering team.

Own the AI platform roadmap and build reusable capabilities (prompting, RAG, tool calling, agents, evaluation, observability). Run discovery, prioritize AI investments, partner with Product, Engineering, Design and Data, guide other PMs, define success metrics, and ensure scalable, reliable AI-powered features that deliver customer and business value.

The Associate Partner will advise C-level executives on AI and Data strategies, lead digital transformations, and develop client relationships in Life Sciences, while driving innovation and overseeing project delivery.

As a Senior AI/ML Engineer, you will develop and deploy AI initiatives, focusing on ML solutions and ensuring robust software integration. Responsibilities include coding, MLOps implementation, Agile delivery, and collaboration with stakeholders to translate business needs into technical solutions.

As an AI Machine Learning Engineer, you'll develop and deploy AI solutions, implement MLOps workflows, and partner with stakeholders to translate business challenges into technical requirements.

Lead AI engineering initiatives by establishing platform strategies, managing talent, enforcing technical standards, and ensuring the operational excellence of AI products.

Lead and execute scalable sourcing and closing strategies, manage full-cycle recruiting, influence hiring goals, mentor teammates, drive process improvements and DEI, and communicate with senior leaders across regions.

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.