Top Artificial Intelligence Jobs in Barcelona

Remote AI/ML Architect to assess current AI/ML landscape, document requirements, and propose SDLC, governance, and deployment approaches. Provide recommendations to architecture and ICT management. Onboarding in the Netherlands (first week). Candidates must be based in Europe with a valid work permit.

Design and document the current AI/ML landscape, capture requirements, propose SDLC approaches and tooling, and advise architecture and ICT management on AI/ML solution design, governance, and deployment. Remote role with first-week onboarding in the Netherlands.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

The Senior Product Manager will lead the strategic roadmap, conduct market research, manage monetization strategies, and mentor a team while collaborating across various departments.

Develop and maintain automation testing frameworks, execute tests, and implement AI-driven quality practices while collaborating with product teams.

Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.

Own and scale full-funnel B2B demand generation: run paid acquisition, optimize landing pages and email funnels, deploy AI/LLM agents for automation, support ABM and field events, and drive measurable pipeline with close Sales collaboration.

Teach and mentor experienced engineers transitioning to system architecture roles: run live sessions and office hours, review student projects and architecture, lead architecture review boards, provide detailed technical feedback, record instructional content, and support a team of instructors.

Lead design, prototyping, and scaling of AI-assisted workflows to optimize content production and growth marketing. Build and deploy no-code automations and AI pipelines across CRM, creative, and messaging channels; streamline delivery processes; manage tooling and vendors; and define operational metrics to track pipeline velocity and efficiency.

Author and iterate curriculum for experienced engineers transitioning to system architecture: validate syllabus, define learning objectives, produce and fact-check AI-generated lessons, design realistic hands-on projects and rubrics, and collaborate with LX designers to refine content over a 4–6 month, ~15–20 hr/week engagement.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Partner with the CEO to turn high-level priorities into execution across product, hiring, licensing, partnerships and country launches. Track OKRs, owners, blockers and deadlines; prepare briefs, updates and action trackers; coordinate cross-functional teams; identify and close execution gaps; and own urgent cross-departmental business problems for the AI neobank expansion in Spain.

Build and ship AI/LLM-assisted features in Ruby on Rails: integrate LLM APIs, implement retrieval/context patterns, ensure reliability, tests, fallbacks, and production safeguards, and maintain system performance, security, and accuracy.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Onboard and support client projects onto Nebius' Azure AI platform using runbooks and golden paths. Adapt Terraform modules and CI/CD pipelines, support ML workload deployment and lifecycle management, capture delivery gaps, and provide structured feedback to Platform Engineering while maintaining security and governance.

Design end-to-end user experiences for autonomous AI agents in a B2B healthcare procurement marketplace. Create human-review workflows, trust and transparency UI, decision/override flows, and rapid prototypes. Work embedded with engineers, test with live users, and iterate based on production failures to optimize agent-human collaboration.

Design, build, deploy, and monitor production-grade autonomous agents for procurement workflows. Own end-to-end lifecycle, instrument logging/monitoring, integrate human-in-the-loop checkpoints, and collaborate with commercial and operations teams to translate real workflows into reliable, cost- and latency-aware agentic systems.

Own end-to-end delivery of AI-powered automations: scope and shape client solutions, build demos and production systems (Python, FastAPI/Gradio, serverless), design prompts and data pipelines, validate outputs, and onboard clients while contributing patterns and R&D to the practice.