Top Artificial Intelligence Jobs in Barcelona

Leads the identification, design, deployment, integration, governance, adoption, and continuous improvement of AI solutions across a Wind Engineering subsystem. Partners with engineering and business leaders to prioritize AI use cases, build roadmaps, configure AI applications and agentic workflows, integrate solutions into engineering processes, validate outputs, ensure security and responsible AI compliance, educate teams, and monitor solution performance and business value.

Lead the end-to-end roadmap for internal AI automation, designing and deploying production-grade agents and multi-step workflows. Integrate agents with core business systems, audit processes, quantify automation impact, redesign workflows with stakeholders, define ROI and performance KPIs, and drive adoption through documentation, training, and change management. The role also evaluates AI tooling, reports progress to leadership, and promotes responsible AI deployment.

Build cloud security training labs and technical content using YAML, Markdown, Bash, and Python. Support training-platform troubleshooting, deliver technical demos and workshops, write courseware, and apply instructional design principles. Use AI coding agents to accelerate lab and script development while validating outputs. Collaborate with senior instructors and cross-functional teams while developing technical depth, teaching ability, and presentation confidence.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend and usage data into clear FinOps experiences, while driving architecture, reliability, monitoring, and iteration. Collaborate closely with customers and product stakeholders, use AI throughout the engineering workflow, mentor peers, and shape product direction in an ambiguous market.

Own the AI cost visibility product end to end, developing Go backend services, APIs, data processing, and TypeScript/React interfaces. Translate AI spending data into clear FinOps experiences, work closely with customers and product needs, and use AI throughout the engineering workflow. Lead feature design, implementation, release, monitoring, and iteration while setting engineering standards, mentoring peers, and building scalable cloud-based SaaS capabilities.

Own AI cost visibility features end-to-end, from customer problem discovery and system design through implementation, release, monitoring, and iteration. Build scalable Go backend services, APIs, and TypeScript/React interfaces that process and present cloud AI spend data. Apply AI throughout the engineering workflow, contribute to product direction, understand FinOps and AI economics, mentor peers, and uphold reliable engineering practices.

Build and deploy production-grade role-based AI agents for renewable energy asset monitoring, analytics, optimization, and decision support. Design multi-agent architectures, integrate diverse data sources and enterprise systems, create evaluation and testing frameworks, implement observability and traceability, and operate AI services in production. Collaborate with renewable energy experts to ensure outputs are accurate, evidence-based, safe, and operationally useful.

Transforms SAP and enterprise data into executive-ready insights on profitability, margins, costs, KPIs, and value creation. Builds analytical models, dashboards, reporting logic, visualization layers, and data refresh processes. Applies AI-enabled tools to improve analytics scalability and collaborates with Finance and business teams to convert findings into measurable performance improvements.

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.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failure modes, and contribute to architectural decisions. The role offers increasing ownership while collaborating with senior engineers, FDEs, FDXs, and enterprise clients in distributed teams.

Build and operate production LLM-powered internal tools, automations, and customer-facing AI features. Own technical decisions, backend services, multi-service architectures, databases, containerized workloads, monitoring, reliability, incident response, and LLM evaluation workflows. Partner with product, security, and R&D teams while balancing AI quality, latency, and cost. Evaluate emerging AI technologies and contribute to reliable, secure production systems.

Build and operate data and AI governance systems, including data quality testing, catalogs, metadata, lineage, ownership frameworks, model inventories, and risk workflows. Partner with data, engineering, legal, privacy, security, and business teams to support GDPR, SOC 2, and EU AI Act readiness. Enable self-service analytics and responsible AI through documentation, access governance, audits, guardrails, and automated quality monitoring.

Lead research and advisory on application security for enterprise applications (ERP/CRM). Produce thought leadership, client-facing insights, presentations, market analysis, and support sales. Mentor analysts, drive research agenda, improve processes, and advise CIOs on security strategy, vendor evaluation, metrics, and vulnerability testing across major vendors (SAP, Salesforce, Workday, Oracle).

Lead research and produce thought-leading insights on application security and AI-enabled development. Advise clients, deliver presentations, mentor analysts, support sales, drive research agenda, and improve processes while providing actionable recommendations and market analysis.

Lead EMEA customers in deploying and optimizing large-scale AI inference on multi-node GPU clusters. Architect dense and Mixture-of-Experts inference pipelines, improve quantization, speculative decoding, prefill/decode disaggregation, KV cache management, and workload scheduling. Collaborate with NVIDIA product teams and engage the developer community through workshops, hackathons, and reference architectures. Advise ML engineers, researchers, and systems architects on high-performance inference across thousands of GPUs.

Serve as the senior technical expert for EMEA AI organizations training and post-training large-scale foundation models. Advise customers on distributed training infrastructure, PyTorch, Megatron-LM, NeMo, reinforcement learning, GPU utilization, communication overlap, and memory management. Build strategic technical relationships, translate customer needs into NVIDIA product roadmap input, collaborate with research and product teams, and support hackathons, demonstrations, and technical conferences.

Serve as a technical bridge between NVIDIA’s generative AI platform and AI-native customers. Guide diffusion model training and deployment, optimize image and video generation pipelines across single- and multi-GPU environments, diagnose vision workload bottlenecks, and provide guidance on model architectures, inference efficiency, video tokenization, and multimodal systems. Translate customer feedback into product improvements and contribute technical demos, workshops, and reference implementations for the EMEA developer community.

Develop and optimize multimodal AI solutions for document intelligence, personalization, and image/video analysis. Build production-ready pipelines, guide customers on model training across modalities, optimize vision encoders and video processing for latency, and translate customer needs into NVIDIA product roadmaps. The role also involves technical relationship management, developer engagement through talks and hackathons, and creating demos and reference architectures.

Build and operate Typeform’s internal AI Operations capability for R&D. Identify and prioritize AI opportunities, discover workflows, design and deliver AI-native solutions, and manage their lifecycle after launch. Responsibilities include improving enterprise AI tools, coordinating access and governance, evaluating reliability and security, supporting automations and agents, measuring adoption and value, and partnering with Product, Engineering, IT, InfoSec, Finance, and other teams.

Lead architecture and hands-on implementation of scalable decision automation and production ML systems for Candy Crush Saga. Build reusable decisioning foundations, improve experimentation and causal inference tooling, mentor senior engineers, and ensure reliability, observability, and cross-team alignment for player-facing personalization and adaptation at scale.

Own the Platform task force roadmap for internal tools, automation, AI agents, security, compliance, reliability, and operational scalability. Map internal workflows, prioritize automation and legacy-system migrations, design permissions and auditability, and measure time saved, cost reduction, risk reduction, and adoption. Partner with Engineering, Ops, Finance, Legal, Data, Care, and product design to align stakeholders and deliver impactful internal products.

Leads the design, development, testing, optimization, troubleshooting, and maintenance of complex full-stack software applications. Collaborates with cross-functional teams, documents technical requirements, resolves IT issues, supports end users, and applies modern engineering practices including microservices, APIs, cloud messaging, CI/CD, containers, Kubernetes, monitoring, and Agile methodologies.

Leads the design, development, maintenance, testing, troubleshooting, and optimization of complex full-stack software applications. Collaborates with cross-functional stakeholders, prepares technical documentation, resolves technical issues, supports end users, and preprocesses data. The role uses C#, .NET, Angular, APIs, microservices, relational databases, messaging systems, CI/CD, containers, monitoring tools, and Agile practices.

Leads the design, development, maintenance, testing, optimization, and troubleshooting of complex full-stack software applications. Collaborates with cross-functional stakeholders, produces technical documentation, prepares data, resolves IT issues, and provides end-user support. The role requires advanced C# and .NET expertise, Angular, APIs, microservices, databases, messaging systems, cloud-oriented engineering practices, CI/CD, containerization, monitoring, and Agile methodologies.