Mohamed Ahmed

• Built multi-tenant AI Learning Companion using FastAPI, authentication, Postgres persistence, Redis-backed state, and hybrid retrieval for agents. • Built citation-grounded ingestion across the knowledge base, preserving timestamps, spatial coordinates, line ranges, hierarchy metadata, and exact references for grounding. • Improved agent reliability by orchestrating tool/function-calling workflows with bounded autonomy, structured outputs, grounded responses, runtime fallback, and failure handling.

• Scaled ingestion across 50+ concurrent feeds: architecting production orchestrators with custom adapters, fingerprinting, Redis queuing, and scheduling. • Increased data-processing throughput by 3x by developing a dual Python/Rust engine using Rayon, Crossbeam, and tiered pattern matching for high-volume pipeline execution. • Security automation workflows spanning feed ingestion, credential-stuffing intelligence, API resilience, and report-ready data normalization for operational security cases.

• Developed a custom T5 Transformer model for antigen–antibody sequence translation/generation using PyTorch and Hugging Face. • Improved model performance by 10% compared to State-of-the-Art (SoTA) models for protein sequence-sequence interactions. • Automated evaluation pipelines and created TensorBoard visualizations, reducing analysis time by 30%.