Shira M.

Responsibilities:

• AI CORE & PERSONALIZATION STRATEGY: Spearheaded the transition from Prompt Engineering to advanced architecture based on Fine-tuning and LoRA. Developed intelligent Agents based on the Gemma-3 (1B/4B) model to emulate unique user styles. Built an automated Python End-to-End Training Pipeline, including dataset creation and SFT (Supervised Fine-Tuning) to continuously improve model quality.

• REAL-TIME ARCHITECTURE & OPTIMIZATION: Developed a synchronous chat system with smart Fallback logic to detect timeouts and maintain flow. Wrote optimized Low-Latency Inference code in Python allowing real-time execution on a single RTX 2080 Ti. Implemented Advanced Resource Management including rapid Adapter Switching and Caching mechanisms to prevent GPU bottlenecks.

• BACKEND & INFRASTRUCTURE MANAGEMENT: Built the server-side in Python using FastAPI to manage system interactions. Integrated coding agents to assist in developing UI components and logic. Managed DevOps & Server Maintenance, handling GPU processes, storage cleanup, and Environment Configuration for the shared development team.

• THEORETICAL RESEARCH & KNOWLEDGE TRANSFER: Conducted an independent Deep Dive into 'Matryoshka Representation Learning', analyzing advanced Embedding architectures and efficiency implications. Synthesized research insights into a professional lecture and comprehensive presentation, delivering the content to a wide forum.

• Implementation of client-side logic for managing chat state Created client-side logic in React and TypeScript for managing chat state, synchronizing messages between players across the dynamic game map, and maintaining consistent communication driven by GEMMA adapters.

• Management of the GPU server Served as the GPU server DevOps hosting GEMMA models, including creating and maintaining bash user accounts, monitoring disk, RAM, and GPU VRAM usage, deploying Docker containers for AI services, and efficiently allocating limited resources across all game services.

• Reading and Deepening Understanding of a Research Paper on Matryoshka Quantization As part of the research, I read and delved into a paper on Matryoshka Quantization, analyzing the core methods, their impact on model performance, and evaluating limitations and potential applications. The work included extracting professional insights, preparing a presentation, and delivering it to an audience, demonstrating a deep understanding of quantization principles and their relevance to complex AI models.