Highly skilled Machine Learning Engineer with over 10 years of experience in developing and deploying high-performance ML systems, specializing in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
Expert in Large Language Models, Retrieval-Augmented Generation, and Production AI Systems
Fine-tuning, optimization, and deployment of transformer models for production use cases
Building scalable retrieval-augmented generation systems with vector databases
Deploying and scaling AI systems with high throughput and low latency
Results-driven Machine Learning Engineer with over 10 years of experience identifying opportunities for business growth and efficiency through advanced data analysis. Proven track record of leading the end-to-end technical execution of data science projects, from defining data acquisition and analytics strategy to deploying production models on cloud platforms (AWS, Azure, GCP).
Expertise in directing junior data scientists, managing stakeholder relationships, and presenting results to senior leadership. Extensive commercial experience in Python, Spark, and deep learning frameworks (PyTorch, TensorFlow) to build and scale ML solutions.
Specialized in advanced machine learning techniques and applications.
Combined statistical methods with advanced computer science applications.
Foundation in engineering principles with focus on electrical systems.
Research paper critically appraising approaches for real-time pothole detection in developing nations.
Read Paper →Exploratory analysis of modified deep learning models for potholes data augmentation at IEEE Conference.
Read Paper →Master's Thesis on efficient hyperparameter optimization for large-scale ML systems.
View Thesis →Research on reducing hyperparameter tuning costs in ML, Vision and Language Model Training Pipelines.
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