Senior AI/ML Engineer with a proven track record of taking AI solutions from rapid prototype to production at scale for global legal organizations. Expert in architecting production-grade RAG pipelines, legal-specific prompt engineering, and AI agent frameworks.
Expert in Large Language Models, Data Engineering, 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
Designing and implementing scalable data pipelines, ETL processes, and data infrastructure
Deploying and scaling AI systems with high throughput and low latency
Senior AI/ML Engineer with a proven track record of taking AI solutions from rapid prototype to production at scale for global legal organizations. Expert in architecting production-grade RAG pipelines, legal-specific prompt engineering, and AI agent frameworks.
Deep hands-on experience in building reliable, grounded AI systems utilizing knowledge graphs (Neo4j), vector databases, and sophisticated evaluation loops (RAGAS, DeepEval). Passionate about delivering trustworthy AI tools that attorneys rely on every day.
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.
Published at ICLR 2024, this work presents a novel approach to reducing hyperparameter tuning costs in ML pipelines.
Read Paper →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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