Vassu Dhingra

• - Developed and optimized machine learning models for diverse applications, including NLP and predictive analytics.
• - Engineered and deployed NLP solutions for text analysis, sentiment analysis, and machine translation using advanced libraries.
• - Fine-tuned and implemented large language models (LLMs) like GPT-4 and BERT for text generation and question answering.
• - Applied Retrieval-Augmented Generation (RAG) techniques to enhance generative AI systems with integrated retrieval mechanisms.
• - Managed and deployed AI/ML models on AWS, utilizing services such as EC2, S3, Lambda, and SageMaker for scalable solutions.
• - Built and maintained data pipelines for large dataset processing with tools like Apache Spark and AWS Glue.
• - Conducted model evaluation, cross-validation, and hyperparameter tuning to optimize performance and accuracy.
• - Collaborated with cross-functional teams to integrate AI solutions and meet business goals.
• - Researched and incorporated the latest AI/ML advancements and best practices into projects.
• - Documented AI/ML project specifications, deployment procedures, and performance metrics; presented findings to stakeholders.
• - Ensured AI/ML solutions complied with security protocols and regulatory standards.

I have extensive experience building recommendation chatbots using Claude 3 and GPT-4, time series analysis models for healthcare, forecasting and prediction, and text extraction for the tire manufacturing industry. My expertise spans machine learning, deep learning, LLM, generative AI, Python API development, and cloud platforms like AWS, GCP, and Azure.

• Optimized machine learning model performance with hyperparameter tuning techniques, achieving improved accuracy and generalization capabilities
• Performed rigorous testing and validation of AI models to ensure robustness and reliability in real-world applications
• Stayed abreast of emerging trends in ML/AI research through active participation in conferences, workshops, and online communities; applied new findings to ongoing projects when applicable
• Conducted thorough code reviews to maintain quality standards across the team''s work while adhering to software engineering best practices
• Developed custom ML/AI models to address unique business challenges, leading to improved decision-making capabilities