Moh Ashraf

AI/ML leader with 10+ years of experience architecting and delivering enterprise scale machine learning systems and full lifecycle MLOps pipelines across NLP, computer vision, recommendation engines, and time-series analytics. Proven track record of deploying generative AI, LLMs and reinforcement learning models that increased predictive accuracy by up to 35%, reduced operational costs by 25%, and enabled autonomous decision making across multicloud and edge environments. Skilled at transforming traditional analytics into cloud native, production grade AI ecosystems that drive measurable business outcomes, scalability and operational resilience. Expert in AI/ML frameworks (PyTorch, TensorFlow, Hugging Face), data and streaming architectures (Spark, Kafka, Flink) and MLOps platforms (Kubeflow, MLflow, SageMaker). Experienced in LLM fine tuning, RAG pipelines, and vector databases, delivering secure, explainable and responsible AI solutions that comply with enterprise governance standards. Adept at leading cross functional teams, optimizing model lifecycles and modernizing AI infrastructure to accelerate innovation and ensure sustainable, high impact adoption of generative AI technologies.