Top Artificial Intelligence Jobs in Barcelona

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Remote SDR responsible for outbound prospecting, qualifying leads, booking demos, maintaining CRM records, collaborating with marketing and sales, and meeting monthly KPIs in the AI SaaS space.

Design, build, and optimize AI-driven automation workflows using platforms like Zapier/Make and LLMs. Integrate APIs, develop Python/JavaScript backend logic, maintain automation pipelines for sales/marketing/support, monitor systems, troubleshoot issues, and document scalable SOPs.

Design, develop, and deploy AI/ML models and LLM-powered workflows (chatbots, copilots, automation). Integrate models with APIs and SaaS platforms, fine-tune prompts, work with structured/unstructured data, monitor performance, and collaborate with product and engineering teams to deliver scalable AI solutions.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

The Senior AI & Computer Vision Software Engineer will work on innovative software solutions, bridge research and production, and mentor others while working in an Agile environment.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

As an AI Creative Producer, you'll develop and scale AI-generated content across various platforms, collaborate with teams, and enhance production workflows.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Develop and deliver AI-powered digital products as a senior full-stack engineer using TypeScript and React. Adapt across full-stack environments, leverage AI tooling and LLMs, understand agent architectures, collaborate across cross-functional teams, communicate clearly in English, and perform in fast-paced, dynamic settings.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Evaluate and generate Spanish-language AI outputs across text, audio, and video; ensure cultural appropriateness, accuracy, and clarity; provide structured annotations and feedback following guidelines; participate in calibration and QA to improve models.

Senior individual contributor who architects, prototypes, and productionizes agentic AI and ML forecasting solutions for Finance. Designs multi-agent systems, ML models for forecasting and anomaly detection, establishes governance/responsible AI practices, and partners with FP&A, Engineering, IT, and Security to deliver measurable automation and forecasting impact.

Embedded in a consulting team, design and implement Python-based AI data pipelines, integrate LLM APIs, build and deploy LLM-powered agents and MCP servers, create reusable automation and multi-agent systems, translate business problems into AI solutions, support project scoping, and communicate technical work to non-technical stakeholders across healthcare and life sciences projects.

Lead enterprise AI strategy, governance, and delivery for regulated life sciences. Own end-to-end AI/data architectures (Azure, Snowflake), mentor engineers, implement MLOps, ensure compliance, and drive adoption and measurable business outcomes.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Lead end-to-end development and integration of AI features (LLMs/ML) into web/mobile apps and APIs. Design AI-first UX, craft and optimize system prompts, build agentic workflows (LangChain/CrewAI/AutoGen), implement error-handling and hybrid cloud/on-prem deployments, and collaborate within product squads using Agile/Scrum.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.