AI/ML Engineer
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AI Engineer and Architect specializing in Multi-Agent, Multimodal RAG with LLM orchestration, building GPU-accelerated, cloud-native platforms achieving ultra-low latency and high accuracy for scalable, production AI solutions.
– Genetic Algorithm (GA): Initialized a population of 50 boolean species masks and evolved them over 100 generations using tournament selection (size 5), crossover rate 0.8, mutation rate 0.1, and elitism (top 5 retained per generation).
– Graph Neural Network (GNN): Designed a 3-layer GNN (hidden dim 64, learning rate 0.001, 100 epochs, batch size 32) to surrogate the simulation-based fitness function. Encoded detailed mechanisms as graphs using message passing with ReLU activations and global mean pooling.
– Fitness Evaluation: Combined validation pass/fail constraints with a species-count penalty. Evaluated reduced mechanisms against full simulations using errors in KPI’s performance.
– Achieved a 75% reduction in species count while retaining 98% simulation accuracy, and improved CO2/syngas operational efficiency by 25%.
