Top Artificial Intelligence Jobs in Seville

As a Senior AI/ML Engineer, you'll design and develop AI solutions, build production-level ML models, and implement MLOps practices. You'll work with a multidisciplinary team on cutting-edge projects, focusing on AI-powered software and deep learning techniques.

Design, implement, and evaluate applied research projects to improve alignment, safety, and evaluation of LLM-based guardrails. Work on experiments (e.g., synthetic data quality metrics, explanations for blocked content), deliver results with mentor guidance, and present findings to the team to shape product capabilities.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.

Build and ship agent orchestration workflows, integrations, and shared infrastructure for AI-driven growth across paid media, lifecycle, organic content, CRO, incentives, and referrals. Implement monitoring, audit logging, creative generation pipelines, and reliable integrations so domain experts can tune and operate workflows independently.

Build and ship native Android features in Kotlin for an AI-enabled neobank app. Design clean user flows, integrate backend APIs, improve performance and reliability, use analytics to iterate, and add on-device AI where it improves UX.

Design, build and ship Android features using Kotlin and Jetpack (Compose). Integrate backend REST APIs, improve performance, stability and memory, and craft clean user journeys for financial services. Use analytics and production debugging to iterate and deliver AI-assisted mobile experiences where beneficial.

Develop and enhance the Ops AI Platform, focusing on building AI agents for operational tasks, integrating them with internal tools, and ensuring user autonomy in operations. Mentor engineers and influence system architecture.

As a Full-stack Software Engineer, you will enhance developer workflows, build AI-native tools, and ensure robust infrastructure for product teams at Alan, focusing on seamless integration and operational excellence.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

As a Senior AI Engineer, you will develop AI-powered features, integrating LLM APIs to enhance user workflows while collaborating with teams to ensure reliability and scalability.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Design, build, orchestrate, and deploy production-ready AI agents and LLM-based solutions. Partner with domain experts to identify high-value use cases, redesign workflows using AI-first principles, integrate AI workflows into business infrastructure, and support internal AI enablement and training.

Partner with Product, Design, Engineering, and Data teams to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and translate analysis into product recommendations and analytics best practices.

The role involves leading design for AI-native products at Synthesia, focusing on improving interaction patterns and enhancing user experience in AI-driven workflows.

Build production-grade AI agent infrastructure for Fever’s Product Engineering organization, including agent harnesses, sub-agents, guardrails, evaluations, knowledge bases, cloud development environments, CI/CD delivery systems, observability, and measurement platforms. Optimize AI workflows for quality, speed, and cost while improving planning, coordination, and communication across engineering squads. Own backend systems end to end and establish engineering standards for reliable production software.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Build and deploy production generative AI, RAG, and agentic systems for enterprise clients. Translate business workflows into technical solutions, develop evaluation harnesses, write production code, and deliver cloud-native systems on AWS. Lead architecture reviews, technical proposals, scoping, monitoring, cost optimization, and client handoff. Partner with senior stakeholders, own technical direction, mentor engineers, and help evolve reusable AI Blueprints across financial services, insurance, healthcare, and life sciences.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

The role involves managing legal and compliance projects, ensuring adherence to AI and e-commerce laws, preparing compliance documentation, and conducting risk assessments while collaborating with the Ethics and Compliance Officer.

Join the Compliance team to own AI and e-commerce legal workflows: GDPR and data protection documentation, AI systems inventory, risk assessments, product/feature reviews, contract and IP issue handling, TOS/privacy updates, and monitoring legal developments. Work with the Ethics & Compliance Officer and review uncensored AI content as needed.