Revamped the Anomaly application UI using React, integrating LLM-powered insights that enhanced user experience and reduced analysis time by 40%. Led development of a confidential AI project utilizing LlamaIndex for RAG implementation, building the entire front-end codebase with real-time LLM interaction capabilities. Implemented advanced MLOps pipelines using TensorFlow and PyTorch for fine-tuning large language models, reducing inference latency by 30% and improving model accuracy by 15%. Designed scalable Node.js architecture supporting 10,000+ concurrent users with vector database integration for semantic search, achieving 50ms average response times. Built real-time data processing pipeline using Kafka and Spark for LLM training data preparation, processing 100,000+ events per second for continuous model improvement.

Led development team building AI-enhanced software applications, implementing GPT-3 integration across Microsoft products with 100% on-time delivery record. Designed ETL pipelines for training data preparation using Python and SQL, optimizing transformer model training workflows and data quality validation. Enhanced search algorithms with transformer-based semantic understanding, implementing BERT models for natural language query processing and improving search relevance by 35%. Developed .NET microservices for AI model serving using Azure ML, implementing efficient message queuing with RabbitMQ for distributed inference requests. Architected data governance solutions on Azure for LLM training datasets, ensuring compliance and implementing automated data lineage tracking for model auditing.

Enhanced search capabilities using Elasticsearch and Node.js with NLP preprocessing, implementing entity recognition and semantic query expansion to improve search precision by 40%. Built intelligent URL validation system in Angular using machine learning models, reducing false positives by 60% and cutting support workload by 25%. Developed Python-based financial simulation engine with natural language interfaces, enabling users to configure complex scenarios through conversational AI, optimizing workflow efficiency. Spearheaded cloud migration from GCP to AWS, implementing containerized ML model serving with Kubernetes, Aurora databases, and infrastructure-as-code using CloudFormation. Created intelligent automation scripts using Python and PowerShell with anomaly detection capabilities, reducing manual intervention by 70% through predictive maintenance algorithms.

Developed Golang-based knowledge management platform utilizing early transformer architectures and graph neural networks for intelligent document processing and relationship extraction. Implemented secure API integration layer using Java for multi-modal data processing, enabling seamless interaction between NLP models and various enterprise databases. Built knowledge graph platform using Golang, Ruby on Rails, and GraphQL, incorporating word embeddings and entity linking for semantic content organization and retrieval. Created intelligent data extraction agents using Golang with custom NLP pipelines, implementing named entity recognition and sentiment analysis for automated content processing. Architected event-driven microservices with Spring Boot and Kafka, designing scalable infrastructure for processing millions of text documents daily with ML-powered content classification.

Built responsive web applications using React with integrated machine learning components, implementing predictive analytics dashboards for client engagement optimization. Developed internal applications using React, Angular, and Node.js with embedded ML models for text analysis, enabling automated insight generation from business documents. Designed RESTful APIs in Java and C# supporting machine learning workflows, implementing efficient data pipelines for training traditional ML models on client datasets. Enhanced employee learning platforms by implementing recommendation algorithms using Python and scikit-learn, personalizing content delivery and improving engagement by 25%. Optimized front-end performance through intelligent caching and predictive loading algorithms, achieving 15% performance improvement in data-heavy analytical applications.