> Markdown version of [/jobs/ext/3094552-senior-ai-engineer-data-ai-organisation-all-genders-merck](https://www.wearedevelopers.com/jobs/ext/3094552-senior-ai-engineer-data-ai-organisation-all-genders-merck). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # (Senior) Ai Engineer - Data & Ai Organisation (All Genders) - Merck - **Company:** Jobrapido - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Programming, Continuous Integration, Information Engineering, DevOps, Python (Programming Language), Machine Learning, Natural Language Processing, Software Tools, Enterprise Data Management, Large Language Models, Snowflake, Grafana, Multi-Agent Systems, Apache Spark, Data Analytics, Integration Frameworks, Machine Learning Operations, Docker - **Published:** September 26, 2026 - **Apply:** https://www.buscojobs.com.es/senior-ai-engineer-data-ai-organisation-all-genders-merck-en-barcelona-ID-372747379 ## About the Role You will contribute to AI literacy and drive innovation across the organization.ResponsabilidadesDesign and implement analytics pipelines and agentic workflowsIntegrate LLMs, predictive models, and multi-agent frameworksEnsure model reliability, reproducibility, and enterprise-scale performanceCollaborate with PODs and cross-functional teams to deliver AI outputs via APIs, dashboards, and embedded analyticsSupport business teams in consuming AI outputs and building user-facing insightsContribute to internal AI literacy through mentoring and knowledge sharingExperiment with new AI approaches and measure impactMaintain awareness of security, explainability, and governance in AI solutionsRequisitos principales5+ years of experience in data science, data engineering, analytics, or visualizationExperience with Finance, HR, or Procurement data and core business processesExcellent programming skills in Python and SQLStrong foundation in ML, statistics, predictive modeling, and NLP with LLMsFamiliarity xqysrnh with vector stores, reranking, and LLM observability tools (Langfuse, LangSmith)Experience with agentic engineering tools (Claude Code, OpenAI Codex, OpenCode)Experience with cloud/enterprise data platforms (AWS, Palantir Foundry, Snowflake) and data frameworks (Spark, polars, DuckDB, dbt)MLOps & DevOps experience (MLflow, CI/CD, Docker)Curiosity about AI trends and strong communication in English; German a plusexcellent communicationcollaborationcuriosityPythonSQLML/AI ## Description OverviewHaga clic en "Solicitar" a continuación para enviar su candidatura.Asegúrese de que su CV está actualizado y de que ha leído primero las especificaciones del puesto.In this AI Engineer role, you design and operate scalable analytics pipelines and enterprise-grade agentic workflows to deliver data-driven AI solutions.You join the Data & AI Organization (MDAO) to partner with Product Owners, Data Engineers, and Data Scientists across functions, translating insights into actionable outcomes.You embed explainability, security, and observability into AI solutions and leverage modern platforms to scale impact.This role offers hands-on work with LLMs, predictive models, and multi-agent systems to address business challenges and strategic data products.You will contribute to AI literacy and drive innovation across the organization.ResponsabilidadesDesign and implement analytics pipelines and agentic workflowsIntegrate LLMs, predictive models, and multi-agent frameworksEnsure model reliability, reproducibility, and enterprise-scale performanceCollaborate with PODs and cross-functional teams to deliver AI outputs via APIs, dashboards, and embedded analyticsSupport business teams in consuming AI outputs and building user-facing insightsContribute to internal AI literacy through mentoring and knowledge sharingExperiment with new AI approaches and measure impactMaintain awareness of security, explainability, and governance in AI solutionsRequisitos principales5+ years of experience in data science, data engineering, analytics, or visualizationExperience with Finance, HR, or Procurement data and core business processesExcellent programming skills in Python and SQLStrong foundation in ML, statistics, predictive modeling, and NLP with LLMsFamiliarity xqysrnh with vector stores, reranking, and LLM observability tools (Langfuse, LangSmith)Experience with agentic engineering tools (Claude Code, OpenAI Codex, OpenCode)Experience with cloud/enterprise data platforms (AWS, Palantir Foundry, Snowflake) and data frameworks (Spark, polars, DuckDB, dbt)MLOps & DevOps experience (MLflow, CI/CD, Docker)Curiosity about AI trends and strong communication in English; German a plusexcellent communicationcollaborationcuriosityPythonSQLML/AI ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [#90DaysOfDevOps - The DevOps Learning Journey](https://www.wearedevelopers.com/videos/548-90daysofdevops-the-devops-learning-journey) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)