Yacin Christian Baltagi

Focused on the development and deployment of Generative AI solutions to streamline internal business operations. Specialized in building secure RAG systems and optimizing LLM inference infrastructure. Customer Intelligence RAG Engine (Self-Hosted): – Evaluated and selected open-source RAG frameworks based on retrieval quality, operational constraints and self-hosting requirements. – Deployed and integrated the final architecture locally to analyze client feedback from diverse sources (Twitter, Zendesk, engagement/unsubscription forms). – Engineered BigQuery extraction pipelines with data cleaning and metadata enrichment (Source, Churn status) to enable precise retrieval filtering. – Deployed a daily automated job to ingest and vectorize fresh feedback, ensuring the Vector Database is continuously synchronized. – Developed a conversational interface (Streamlit) allowing non-technical teams to extract insights in natural language, significantly reducing time-to-insight. LLM Production & Infrastructure (LLMOps): – Designed and deployed secure FastAPI microservices to expose internal, self-hosted LLM endpoints, replacing legacy ML models. – Developed robust API contracts using Pydantic for data validation and auto-generated OpenAPI/Swagger documentation. – Led strategic model evaluation (Open Source vs. Proprietary), benchmarking Cost vs. Performance to assess when LLM-based endpoints could replace or complement legacy ML systems.

Focused on Machine Learning lifecycle management (MLOps), workflow automation for business units, and data engineering. Workflow Automation: – Developed sophisticated Python algorithms to automate complex, repetitive tasks for the Legal and Finance departments significantly reducing human error and freeing up valuable time for business teams. – Built accessible internal tools using Neo4j (Cypher), SQL, Python and Streamlit (frontend), allowing non-technical users to visualize and query complex graph data relationships without writing code. Machine Learning & MLOps: – Designed, implemented, and deployed predictive models (Fraud detection, Spam classification). – Managed reproducible pipelines with ZenML and handled infrastructure provisioning on GCP using Terraform for secure, scalable deployment. Data Engineering & BI: – Structured robust data models using dbt and SQL within a BigQuery environment. – Developed interactive dashboards on Omni and Metabase to empower stakeholders with actionable strategic insights.