Top Artificial Intelligence Jobs in Barcelona

Design and build agentic AI systems that analyze ServiceNow Discovery logs, classify failures, perform root cause analysis, monitor infrastructure health, and recommend corrective actions. Develop production-grade Python services, REST APIs, integrations, data pipelines, observability capabilities, and CI/CD workflows. Apply LLM APIs, RAG, vector databases, infrastructure troubleshooting, and network protocol expertise to improve discovery reliability at enterprise scale.

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.

Lead design and delivery of an agentic AI Fabric: runtimes, orchestration, model gateway, and MCP/A2A integration spine. Own agent identity/entitlement, audit and GxP/Part 11 compliance, shared eval harness, and manage AI Harness Engineers. Drive architecture, standards, and enterprise adoption of agentic workflows and safe sandbox-to-certified promotion processes.

Design and develop scalable backend services, integrate AI capabilities, and improve existing systems. Collaborate across teams to enhance the CMC platform.

Transcribe and annotate Catalan audio/video, perform data labeling and classification, and record voice samples. Ensure native-level fluency, accurate grammar/spelling, attention to detail, and meet project quality and speed requirements.

Remote contributors will transcribe and annotate audio/video, perform data classification and labeling, and record voice samples in Singlish. Must be fluent in spoken and written Singlish, accurate with grammar/punctuation, follow project guidelines, and meet productivity and quality standards.

Lead technical qualification, design, and delivery of AWS-based data, analytics, ML and AI solutions. Translate requirements into scalable AWS architectures, drive complex projects, contribute to SOWs and handovers, identify risks and opportunities, and mentor colleagues while building repeatable services and accelerators.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Lead global product and engineering teams to design, build, and operate an enterprise-grade AI/ML platform for scientific research. Own strategy, partner with internal and external stakeholders, ensure security, GxP compliance, scalability, cost management, and developer experience across ML infrastructure, SDKs, tooling, and deployments.

Designs, builds, deploys, and governs production AI solutions, including agentic systems, RAG applications, reusable AI skills, orchestration patterns, evaluation frameworks, and observability capabilities. Partners with scientific, commercial, and operational stakeholders to translate workflows into reliable solutions. Establishes testing, monitoring, governance, security controls, audit trails, and human-in-the-loop processes for regulated use cases. The role also documents reusable assets, influences platform standards, and upskills domain practitioners.

Build and scale production AI applications, agents, retrieval systems, AI-ready data products, and platform capabilities. Responsibilities include RAG, vector search, semantic enrichment, data governance, quality, lineage, evaluation, monitoring, observability, and enterprise integration. Collaborate with domain experts, engineers, platform teams, and business stakeholders to create reliable, reusable AI solutions that address complex business needs.

Build and maintain production LLM systems: design agentic multi-step workflows (LangChain/LangGraph), manage RAG pipelines (parsing, embeddings, vector DBs), develop and version prompts, implement evaluation frameworks for hallucination and grounding, and apply CI/CD, testing, logging, and observability. Work closely with AI and engineering leadership in a hybrid Barcelona role requiring English and Spanish fluency.

Operate and support large-scale AI infrastructure: monitor production GPU-backed Kubernetes platforms, troubleshoot infrastructure, networking and hardware incidents, participate in incident response and RCA, improve observability and automation, and maintain runbooks and operational documentation.

Design, build, and deploy agentic AI and multi-agent systems for end-to-end supply chain automation. Integrate LLMs, RAG, and vector databases, implement cloud-based production solutions with MLOps, CI/CD, Docker/Kubernetes, and measure operational impact.

Design, deploy, and maintain agentic AI systems that integrate security into engineering workflows. Own internal security agents, cloud security posture, telemetry filtering, automation, SDKs, and integrations with endpoint, vulnerability, identity and GRC tooling. Provide guidance, participate in incident response/on-call, and partner with engineering leadership to drive low-friction, agent-driven security improvements.

Design and build agentic AI systems to autonomously triage and remediate security findings, enforce endpoint security baselines across multiple OSes with MDM and EPP tools, automate security workflows, coordinate cross-functional automations, own intelligence/triage logic, participate in incident response and on-call rotations, and iterate on metrics to reduce remediation time and improve compliance.

Senior hands-on SRE focused on building and running AI/ML platform tooling and observability. Design instrumentation, alerts, telemetry pipelines, automation, AI-augmented operations tooling, lead incident response and post-mortems, mentor L1/L2 engineers, and drive platform operational readiness and continuous improvement.

Senior hands-on SRE responsible for designing and running AI/ML platform observability, automation, and incident processes on Azure/AWS. Build telemetry, alerts, runbooks, AI-augmented ops tooling, lead complex incidents, drive automation to reduce toil, onboard platforms for operations, and mentor L1/L2 engineers to raise operational standards.

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

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.

Design, build, and operate backend services for an AI-driven financial assistant. Own full SDLC, ensure performance, scale, resilience, and collaborate in small autonomous teams.

Lead execution of the AI transformation strategy: prototype and scale AI-powered services, own revenue for innovation offerings, build repeatable piloting frameworks, implement AI agents internally, and partner cross-functionally to drive commercial impact and operational efficiencies.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Design and build an AI-native HR platform: backend systems (Python/Django/ASGI), integrate LLMs and agent frameworks, implement RAG/vector search, telephony/voice interfaces, and mentor junior developers while ensuring scalable, low-latency production deployments.

Design, build, and deploy LLM-powered solutions and scalable backend systems. Implement data extraction, semantic search, document analysis, agentic flows, and evaluation frameworks. Continuously evaluate, tune, and improve model performance while collaborating with product, design, and legal teams.