Top Artificial Intelligence Jobs in Barcelona

As an AI Strategist, you'll deploy AI agents for healthcare clients, optimize operations, drive performance improvement, and develop internal frameworks.

Design, train, and deploy LLM-powered agents and recommendation/ranking models from large-scale logs; build recommendation APIs; support CI/CD for model/data versioning; design offline metrics and assist online A/B testing; collaborate in Agile teams and monitor SLAs/KPIs.

Join Entrust's AI team to define, prototype, and implement AI/ML solutions and agentic AI features. Collaborate cross-functionally to build scalable, documented systems with REST APIs, CI/CD, containerization, testing, and cloud deployments, advancing Pilot→MVP→Production innovation.

Build production AI-native systems for BJAK's neobank: AI agents, RAG/LLM apps, tool-calling, automations and copilots for onboarding, KYC, support, risk, CRM and operations while addressing accuracy, hallucination, latency, cost, security and compliance.

Develop, fine-tune, and productionize ML models for mobile apps. Own data curation, experimentation, evaluation, deployment, and integration with mobile/backend teams while tracking quality metrics.

Design and scale AI systems for healthcare conversations, ensuring low-latency performance and reliable orchestration of speech and LLM pipelines.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

The AI Software Engineer will develop and improve Joko's AI shopping assistant, focusing on architecture, evaluation, data management, and personalized user experiences, while collaborating with product teams.

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.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

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.

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.

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.

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.

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.

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, 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.