Dr. Mirza Klimenta

Objective: Creation of a multi-agentic system that helps a media house in their daily business. The first project was creating a RAG system via Google Filestore, ingesting data from more than 80 websites (belonging to this media house) - this system was then queried by the end uses, who would ask various questions about the new content (series, shows, movies, books). Another project was developed with Google Agent Development Kit (ADK), and was a multi-agentic system communicating with RAG, on top of which I build an analysis layer, reporting about the recent job posts suitable for the media house. I was responsible for the design and end-to-end development,

including deployment at GCP.

Objective: To create a multi-agentic system, supported by a Knowledge Graph, that automates the process of drafting a research paper. The system used multiple experts(OpenAI models) that ”collaborated” during the process of document drafting. The whole process was supported by a Knowledge Graph out of which we extracted useful information. Technologies used for this framework: LangChain, LangGraph, Smolagents, LlamaIndex, dspy. The project involved the use of Terraform and GitHub Actions (CI/CD Pipeline), via AWS. The initial application was deployed as a Streamlit app. I was responsible for the AI Engineering part, Knowledge Graph creation, and also deployment in AWS. This was a GenAI Engineer role, also involving Prompt Evaluation and Retrieval Optimization (Langfuse).

Objective: given a spectral image that depicts the value of the sensors’s readings,

classify the signals and identify novelties (anomalies).

Approach: Given that there was not sufficient labeled data, I had to rely on self-

supervised machine learning paradigms. To satisfy the customer’s request for fast

processing, I utilized one of the YOLO architectures. The system was developed

with the mmyolo framework.