Filipp Trigub
AI Engineer
Experience
CTO
Infolab.ai
Title: Multi-chain LLM copilot for academic teaching and studying
Goal:
– Build a sophisticated AI copilot to augment the students’ learning experience and provide AI-derived insights to professors.
Solution:
– Build a multi-chain LLM system adapting to user needs at its own accord with a Weaviate vector DB based RAG system and evaluated it with Ragas.
– Build responsive react frontend, and backend systems handling auth, data management and auxiliary services as a RESTful API.
– Deployed and managed the app to the cloud in a production environment including the CICD via multi-stage deployment.
Lead Developer
Hostafrica
Goal:
– Provide a GPT-powered chatbot using internal documents to support service staff and interact with customers directly.
Solution: – Led the design and development of a lightweight GPT-powered chatbot for service-staff support for an international client.
– Contributed significantly to the initial design, technology selection, and architecture.
– Implemented a haystack pipeline with OpenAI embeddings to optimize GPT usage and integrated CI/CD and DevOps for rapid, collaborative development.
– Spearheaded technical development and introduced agile development practices, setting guidelines and conventions for the team.
Lead Developer
ECB
Goal:
– Design and develop an application for statistical data analysis holistically and support users on-demand.
Solution:
– Designed and developed architectural extensions based on client demands and limitations.
– Successfully implemented over 500 stories in a 300k+ LoC codebase, which included a dockerized Python backend hosted in AWS with PostgreSQL and Oracle DB, as well as a JS frontend, both delivered via CI/CD.
– Ensured continuous stability through extensive unit and end-to-end testing.
– Served as Scrum Master for a team of 7 developers for over a year
Summary
Languages
Education
Humboldt University Berlin
Master of Science · Physics · 3.6
Thesis:
– “Makovian and Non-Markovian Dissipation Mechanisms in Nonequilibrium Dispersion Forces.”
– Modelled stochastic processes with memory effects mathematically. Validated the model numerically using an implementation in C.
Relevant coursework
– Statistical Data Analysis: Mathematical foundation and practical application of data processing and machine learning on real world data in python notebooks.
– Statistical Mechanics: Theoretical Statistics at an advanced level with coding exercises.
– Fluctuation Induced Phenomena: Deep understanding of complex processes driven by randomness fostered by advanced mathematical and numerical exercises.