AI Engineer / Generative AI Consultant

Built a production-ready multi-agent AI system enabling natural language data analysis for enterprise users.

Designed stateful workflows with LangGraph, coordinating specialized agents for retrieval, analysis, visualization, and planning.

Implemented a hybrid AI architecture using SQL and vector search on LanceDB, with autonomous Python code generation for statistical analysis and visualizations. Deployed the solution with Docker and Gradio, supporting human-in-the-loop workflows and a high user approval rate.

Developed and deployed scalable multi-agent AI systems for a pharma industry into production using LangGraph, LangChain, and Azure AI Services.

Led the development of AI solutions including RAG architectures and integrated them into Azure-based systems to improve the accuracy and relevance of AI outputs.

Managed and expanded the Generative AI asset repository on Azure DevOps, ensuring robust MLOps practices and CI/CD pipelines for reusable and maintainable AI components.

Led an innovative Generative AI proof of concept with large language models, using OpenAI API and Azure to improve data-driven insights and customer experience.

Created an MVP that improved business managers' operational workflows and significantly reduced task completion time.

Worked on DevOps and MLOps practices with Azure, including CI/CD pipelines and Docker.

Led the development of a Transformers-based AI tool for real-time customer feedback analysis using large language models and NLP.

Optimized BERT models to achieve strong sentiment analysis performance and support better customer engagement and decision-making.

Contributed to marketing insights by improving the analysis of customer feedback data.

Led Industry 4.0 initiatives using Python and IoT.

Delivered annual cost savings per manufacturing line and demonstrated versatility in technology applications, including predictive maintenance.

Supported digital transformation efforts focused on operational efficiency and industrial innovation.

Completed consulting-focused internal training covering problem analysis, business models, agile methods, and business case definition.

Participated in innovation projects using design thinking methodology and collaborative teamwork.

Strengthened cross-cultural collaboration and user-centered innovation skills.

Master's program focused on data science and business intelligence. Covered machine learning, deep learning, NLP, cloud computing, big data, SQL, NoSQL, and DevOps-related topics.

Specialization in data analysis and Industry 4.0.

Master's degree in industrial engineering. Included an Erasmus period at Luleå University of Technology in Sweden.