Job main responsibilities:

- Support development and deployment of AI and advanced analytics use cases

- Prepare, validate, and curate data for AI workloads

- Ensure quality, performance, and governance of AI outputs

- Collaborate with data engineering and analytics teams

- Support responsible and scalable AI adoption

Technical Skills & Technology Landscape:

- Machine learning and advanced analytics concepts

- Analytics-ready and AI-ready datasets

- Model validation, monitoring, and performance tracking

- Cloud-based analytics and AI platforms

Qualifications and Skills:

1. Core Technical

- LLM orchestration frameworks: LangChain, Semantic Kernel, Azure AI Foundry

- MLOps practices: model versioning, deployment pipelines, monitoring (MLflow, Azure ML)

- Prompt engineering: few-shot, chain-of-thought, structured output, retrieval-augmented generation (RAG)

- Azure AI Services: Azure OpenAI, Cognitive Services, AI Search (vector and hybrid)

- Feature engineering and ML pipeline development (Databricks Feature Store, MLflow)

- Responsible AI: bias detection, explainability, AI governance frameworks

- AI-ready data design: embedding generation, vector store management, data curation for AI

- API integration: exposing AI capabilities as enterprise services (FastAPI, Azure API Management)

1. Certifications

- Microsoft Certified: Azure AI Engineer Associate — Preferred

- Microsoft Certified: Azure AI Fundamentals — Preferred

- Databricks Certified Machine Learning Professional — Preferred

- Generative AI for Business Leaders (Microsoft / Coursera / DeepLearning.AI) - Strongly Preferred





- Microsoft Certified: Fabric Analytics Engineer Associate — Preferred

1. Industry & Business Knowledge

- Industrial AI use cases: predictive maintenance, quality control, demand sensing

- SAP data context for AI inputs: finance forecasting, procurement analytics, production data

- Responsible AI governance in a general manufacturing enterprise

- Understanding of data privacy, AI regulation (EU AI Act), and compliance requirements

- Business value framing: translating AI capabilities into operational impact

1. Behavioral & Leadership

- Innovation mindset balanced with pragmatic delivery

- Ability to translate AI concepts for non-technical business audiences

- Responsible AI advocacy — champions governance alongside capability

- Hypothesis-driven experimentation: tests before scaling

- Strong cross-domain collaboration with data engineering, analytics, and business units

What do we offer?

- HybridWorkModel :Flexibilitytoworkfromhomeandintheoffice,accordingtothepolicy,helpingyouachieveahealthywork-lifebalance.

- TicketRestaurant :Enjoyadailymealallowancetosupportyourwell-being.

- Flexibleretribution :Kindergarten&Transport;

- 30 Labor Days of Holidays :Ampletimeofftorelaxandrecharge.

- LanguageLessons :Accesstolanguagelessonstohelpyougrowbothpersonallyandprofessionally.

- MedicalInsurance : 60% company-subsidized medical insurance for employees, with the option to extend coverage to family members at a highly competitive rate.

- OpenandModernOfficeEnvironment :Workinacollaborative,innovative,andcomfortablespacedesignedforyoursuccess.

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📌 AI Lead (Barcelona)
🏢 Omya
📍 Barcelona