Lola Assad Van Cover

Lead Artificial Intelligence Engineer at Flash Pharmacy

Engineered and managed end-to-end AI product ecosystems, ensuring seamless transition from development to high-availability production with rigorous performance standards. Architected and deployed 20+ production-grade AI tools and MCP servers focusing on multimodal agentic systems for healthcare, medicine, and pharmaceuticals. Implemented guardrails to mitigate hallucination and bias, actively monitoring outputs in production. Designed scalable ETL pipelines for Retrieval-Augmented Generation (RAG), enabling high-fidelity data retrieval for complex AI-driven solutions. Orchestrated model deployments within Azure, implementing cost controls and quota management to optimize resources without sacrificing performance. Led cross-functional engineering teams through the full SDLC, integrating custom-built APIs with high-performance front-end applications. Partnered with executive leadership to align AI roadmaps with business objectives and reported progress to stakeholders.

Researcher at Technische Hochschule Ulm

Awarded a DAAD Science and Engineering scholarship to conduct research on Thrombectomy Assist, an AI model comprising a CNN, an RNNq, and a deep learning classifier to determine thrombus-free status from angiography image data. Collaborated with a team of four surgeons and technicians to understand thrombectomy workflows and integrate the model into the existing procedure. Presented developments at the Imaging Network of Ontario Symposium 2025 (attended by over 200) and prepared an abstract for publication.

Researcher and Outreach Coordinator at Percutaneous Surgery Laboratory, Queen’s University

Continued supporting the Thrombectomy Assist project under the DAAD RISE framework, achieving accuracy above 90%. Managed logistics and coordinated volunteers for 5+ outreach events showcasing computer-assisted surgery to attendees, distinguished professors, prospective students, and researchers.

Teaching Assistant - Artificial Intelligence, and Discrete Mathematics at Queen’s University

Designed an AI Ethics module for the 2026 CISC 352 class, facilitating understanding of responsible AI engineering. Taught and mentored 250 students on topics including AI basics, deep learning, machine learning, planning, constraint satisfaction problems, and mathematical proofs. Graded assignments, created rubrics for proving mathematical correctness, supervised a student project on AI cancer detection scoring 95%, and led a tutorial on modular arithmetic, propositional logic, truth tables, universal quantifiers, and proofs.