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Lead and manage a team of 12 ML engineers and project managers, driving the company’s Machine Learning and Large Language Model (LLM) strategy and roadmap. Responsible for shaping the vision, defining requirements, and ensuring successful delivery of cutting-edge AI solutions. Key Responsibilities & Achievements: • Strategic leadership of the ML Cluster, with a strong focus on LLM-driven innovation and integration into products. • Recruitment, mentoring, and development of ML engineers, fostering advanced expertise in LLMs, NLP, and deep learning. • Definition of project requirements and specifications, aligning ML initiatives with business goals. • Oversight of ML infrastructure design, deployment, and continuous improvement to support scalable LLM applications. • Assignment and leadership of project managers and engineers, ensuring effective execution of complex ML projects. • Organization of workshops, knowledge-sharing sessions, and team-building events. • Promotion of research excellence through publications, conference participation, and collaborations with leading academic and industry institutions. • Recognition as the principal ML/LLM expert within the team, providing technical guidance and thought leadership.

Contributed to multiple cutting-edge research projects at the intersection of Deep Learning, Neuro-Symbolic AI, and Explainable AI, with applications in healthcare, genomics, and industrial systems. Key Projects & Contributions: • Neural-Symbolic Systems for COVID-19: Developed hybrid AI models combining deep learning and logic-based reasoning to identify patients in critical states. • Explainable AI Systems: Conducted research on scalable, interpretable AI by integrating symbolic logic with neural networks for transparent decision-making. • Genomic Data Analysis: Applied Deep Convolutional Neural Networks to detect signatures of natural selection in large-scale genomic datasets.• Neural Networks on FPGA: Implemented optimized architectures on Xilinx Ultrascale FPGA for real-time image processing applications. • Gas Turbine Fault Prediction: Built ML models (Neural Networks, Decision Trees, Extra Trees, Random Forest) to predict sudden stops in gas turbines, enhancing predictive maintenance. Academic Contributions: • Served as reviewer for leading journals and conferences, including IJCAI, Frontiers in Artificial Intelligence, and Food and Bioproducts Processing.