AI Engineer

• Enablement & coaching — work directly with engineers through 1:1s, pair programming, workshops, office hours, and cohorts to raise their effectiveness with AI-assisted development: prompting, workflow design, and model/tool selection. Identify under-adoption and lift teams up to the standard.
• Best-practice propagation — capture what works (and what fails) in one team and systematically transfer it: living best-practice guides, prompt libraries, troubleshooting playbooks, and an internal AI knowledge base and skills registry.
• Scale through champions — stand up and support a network of embedded AI champions so enablement scales past a single person.
• Unblocking & feedback loop — be the go-to when engineers get stuck with AI tooling. Proactively tour teams to surface blockers early, aggregate them into patterns, and escalate systemic issues to leadership.
• Internal integrations & tooling — build internal AI integrations and reusable tooling hands-on (Node.js/TypeScript, MCP) — from prototype to production independently, including auth, access control, security review, and CI/CD.
• Evaluation & scouting — run structured evaluations of AI tools, models, and internal integrations; monitor the fast-moving landscape (Claude Code, Cursor, Copilot/Codex, new model releases) and deliver curated, actionable recommendations.
• Measurement & impact — define and track adoption and productivity metrics by team, tool, and individual. Build dashboards on real usage and output — adoption/active-usage rate, assistant acceptance rate, PR throughput and cycle time — with a quality guardrail (change failure rate) so speed never comes at the cost of quality. Reference frameworks: DX Core 4, DORA.
• Cross-functional partnering — partner with security/compliance on safe-by-design guardrails, and with GTM to translate field needs into internal enablement and feed adoption patterns back.

Senior AI Enablement Engineer

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Application Developer – AI Finance Agent

• Own end-to-end delivery for complex AI engagements
• Translate business challenges into technical solutions, lead solution design and deployment
• Operate as a trusted technical partner to customers
• Own delivery end-to-end, from scoping to production
• Troubleshoot and enhance system performance, scalability, and reliability
• Capture deployment learnings, document best practices, and proactively share insights
• Adapt quickly to emerging technologies and evolving customer requirements
• Actively contribute to the evolution of Kyndryl’s AI platforms

Lead Agentic, AI Architect

• Build and deliver AI solutions: Take ownership of developing delightful, high-performance AI features to help users discover, organize, and optimize access to large datasets.
• Rapid experimentation and iteration: Implement a highly iterative process where you quickly prototype, test, and validate with real users, including shipping and evolving LLM- or agent-powered workflows for the data engineering lifecycle.
• Collaborate: Work with the rest of the team to shape AI-driven product features, including the integration of agentic components with internal tools like Slack and alerting systems while engaging with internal data teams for dogfooding.
• Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows.
• Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams.
• Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.

Staff AI Engineer

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems.
• Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows.
• Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence.
• Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production.
• Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust.
• Turn manual processes into reliable AI-enabled systems that can scale.

Applied AI Engineer

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Design and build AI-powered applications and agents on the Azure AI stack (Azure AI Foundry, Azure OpenAI)
• Develop agent orchestration, RAG pipelines and tool/function calling in C#/.NET using Semantic Kernel or Microsoft Agent Framework
• Integrate LLM capabilities into enterprise applications and services (APIs, event-driven architectures)
• Build and maintain knowledge bases and retrieval layers with Azure AI Search, embeddings and vector stores
• Implement evaluation of AI outputs (test datasets, quality metrics, prompt iteration) so solutions are measurable, not just plausible
• Apply enterprise-grade security, identity (Microsoft Entra ID), networking and observability practices to AI workloads
• Automate build, deployment and analysis workflows with Azure DevOps pipelines
• Work embedded with client technical teams in an Agile / Scrum environment
• Present progress, solutions and recommendations to international stakeholders in English
• Document architectures, patterns and decisions, and propose continuous improvement

AI Engineer – Semantic Kernel, Microsoft Agent Framework

• Leverage data to solve business problems of various business units at EcoVadis
• Design, develop, deploy and maintain scalable AI/ML systems
• Design AI/ML pipelines by applying the best practices in MLOps / LLMOps / AgentOps
• Build AI/ML engineering infrastructure and systems to orchestrate batch and real-time pipelines
• Run large-scale experiments and tests to ensure quality and efficiency of ML and data pipelines
• Partner with scientists and engineers to make AI/ML models accessible to end-users and downstream processes
• Leverage Python, MLflow, Azure stack (e.g., Azure cloud, Azure ML, Azure Foundry) and Databricks to deliver end-to-end solutions

Senior AI/ML Engineer

Mobile Developer – AI Neobank App

• Deep-dive into business units (Finance, Sales, HR) to uncover manual bottlenecks.
• Use LLMs, low-code/no-code AI tools, and automation platforms (n8n, Lovable) to build functional MVPs immediately.
• Own the full lifecycle of internal tools: from discovery and scoping to building and deployment.
• Act as the bridge between business requests and technical builds.
• Document requirements and bridge the gap to official IT/Engineering teams for enterprise scaling.

AI Operations Engineer, Functional Analyst

• Provide technical leadership for AI engineering across the Consumer experience AI Team by establishing and coding standards.
• Improve development practices and accelerate delivery of scalable AI solutions.
• Ensure consistency, quality, and speed across all AI engineering activities.
• Define and enforce coding standards, design patterns, and engineering best practices for AI solutions.
• Lead technical design reviews and code reviews across AI use cases and platforms.
• Guide the team in implementing scalable architectures (RAG, agents, orchestration patterns).
• Improve and standardize the development lifecycle (CI/CD, testing, release processes).
• Identify and reduce technical debt, improving maintainability and performance.
• Drive adoption of new technologies and engineering approaches in AI.
• Mentor team members and support their technical growth.

Tech Lead – AI Engineering

• Diseñar, desarrollar y desplegar aplicaciones y soluciones basadas en Inteligencia Artificial y Machine Learning.
• Participar en todo el ciclo de vida del desarrollo de software, desde el diseño hasta la puesta en producción.
• Construir soluciones robustas, mantenibles y escalables siguiendo las mejores prácticas de ingeniería de software.
• Optimizar algoritmos y mejorar el rendimiento computacional de aplicaciones de IA.
• Colaborar con científicos de datos, consultores y equipos de negocio para transformar necesidades funcionales en soluciones técnicas.
• Guiar a perfiles no técnicos en buenas prácticas de desarrollo y arquitectura de software.
• Desarrollar soluciones cloud-native utilizando plataformas como AWS, Azure o Google Cloud.
• Implementar pipelines de despliegue y automatización mediante herramientas DevOps.
• Participar en iniciativas de innovación, investigación y generación de conocimiento en Inteligencia Artificial.
• Colaborar con clientes internacionales en proyectos de transformación digital.

• Deploy machine learning models that support game features, player experience, and operational efficiency.
• Improve the code quality of others' work by refining SQL and Python scripts for data transformation, feature engineering, modelling pipelines, and AI app, thereby ensuring production readiness.
• Explore and contribute to early-stage projects involving LLMs, RAG systems, and agentic AI to power new types of automation and decision support.
• Develop or refine Streamlit-based tools (or alike) that present ML/AI capabilities to non-technical users via intuitive and interactive interfaces.
• Help build and implement model monitoring systems to ensure long-term performance and business alignment.
• Collaborate with teams across product, data, and engineering to identify where ML and AI can bring value.
• Communicate results, limitations, and recommendations clearly across technical and business collaborators.

ML/AI Engineer

• - Lead technical integration projects with strategic clients: understand their infrastructure, design integration architectures, and drive implementations to completion.
• - Customize and configure Idoven's platform for client-specific clinical use cases and deployment environments.
• - Diagnose and resolve complex technical issues in live client environments — sometimes under time pressure.
• - Attend client meetings alongside commercial and clinical teams; provide technical credibility and translate AI capabilities into clinical and business value.
• - Build integration documentation, technical runbooks, and reusable assets that enable clients to scale without constant Idoven involvement.
• - Act as a feedback loop: surface product gaps, integration challenges, and technical debt back to Idoven's engineering and product teams.
• - Collaborate with the solutions engineering and clinical teams to improve the platform's scalability and configurability.

Forward Deployed Engineer – Healthcare AI

• Diseñar y construir arquitecturas de IA generativa y LLMs personalizadas end-to-end, sobre el ecosistema Microsoft (Power Platform, Dataverse, Azure, Fabric...).
• Evaluar la viabilidad técnica de los casos de uso y trazar la hoja de ruta tecnológica junto a equipos de negocio y datos.
• Desarrollar, adaptar y poner en producción soluciones avanzadas de IA, ajustando y afinando modelos existentes sobre datos propios (fine-tuning, retraining).
• Integrar la IA con sistemas empresariales y plataformas de datos, impulsando la adopción cross-functional.
• Colaborar de forma activa y directa con equipos internacionales (España, Suiza, Alemania) y multidisciplinares, aportando visión técnica y de producto.
• Ser referente técnico y hands-on, sin perder de vista la posible evolución a team lead en el futuro próximo.

AI Architect – Senior AI Engineer, Microsoft Ecosystem

• You own AI capabilities end-to-end: from the data and context that feed a model, through agent orchestration, to the generative UI a user sees and trusts. You've personally shipped and operated full-stack apps in production.
• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols
• Ground models in real data: retrieval, embeddings, and feedback loops that actually close
• Treat evals as a first-class system - provable improvement, not vibes
• Take a capability from prompt-in-a-notebook to a measured, guard-railed production feature

• Design and build AI-enabled features for various education products, from assessments to tutoring.
• Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints.
• Build evaluation approaches that measure output quality, consistency, usefulness and failure modes.
• Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate.
• Integrate LLM APIs, AI services and education data into reliable product capabilities.
• Design practical human-review, control and escalation workflows for AI-supported decisions.
• Improve the reliability, latency, cost and quality of AI services in production.
• Build prototypes quickly, test them against real education use cases and productionise successful approaches.
• Develop suitable guardrails, monitoring and auditability for AI outputs.
• Work with Backend Engineers on service architecture, data handling, integrations and scalability.
• Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students.
• Use AI-assisted development tools appropriately while maintaining strong engineering judgement, testing, privacy and security standards.
• Write clean, tested and maintainable code.

• Diseño, desarrollo y evolución de sistemas de IA generativa (agenticos y RAG) en producción
• Integración de modelos y agentes con plataformas y sistemas de negocio

AI Engineer – Híbrido Madrid

• Diseñar arquitecturas y buenas prácticas agénticas para IA Generativa sobre ecosistema Microsoft Azure.
• Definir estrategias de adopción de IA en entornos empresariales.
• Interlocución con perfiles de liderazgo y negocio para definir requisitos y estrategia.
• Comunicación oral y escrita fluida en inglés.

AI Engineer jobs in Spain

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Senior AI Enablement Engineer

Mobile Application Developer – AI Finance Agent

Lead Agentic, AI Architect

Staff AI Engineer

Full Stack Software Engineer – AI Neobank App

Applied AI Engineer

Applied AI Engineer – AI Neobank App

AI Engineer – Semantic Kernel, Microsoft Agent Framework

Senior AI/ML Engineer

Mobile Developer – AI Neobank App

AI Operations Engineer, Functional Analyst

Tech Lead – AI Engineering

AI Engineer

ML/AI Engineer

Forward Deployed Engineer – Healthcare AI

AI Architect – Senior AI Engineer, Microsoft Ecosystem

AI Engineer

AI Engineer

AI Engineer – Híbrido Madrid

Senior AI Engineer