• Fine-tuned and deployed advanced language models such as BERT, GPT-3, and Claude3 using Electronic Health Records (EHR) datasets to enhance the understanding and management of medical data, leveraging frameworks compatible with TensorFlow and PyTorch.• Developed Retrieval-Augmented Generation (RAG) models with LangChain, integrating them with multiple large language models (LLMs) to improve information retrieval and generation in healthcare applications.• Evaluated and optimized models including T5 and BioBERT with healthcare-specific metrics to ensure high accuracy in EHR data processing, ensuring data governance standards.• Integrated AI models with healthcare systems utilizing Flask for seamless deployment, collaborating across teams to ensure scalability and efficiency in production environments.• Designed and managed APIs and server-side functionalities for efficient model performance and data handling in production environments, facilitating integration points with AWS services.• Managed vector databases, such as Pinecone and SQL, for efficient EHR data storage, retrieval, and integration, enhancing data governance and security compliance.• Explored and implemented tools like Med-PaLM to improve data analysis in medical contexts, driving innovation through generative AI methodologies.• Conducted data preprocessing and transformation using Python libraries and LangChain to provide high-quality input data for AI models, supporting complex ML pipelines with an emphasis on reliable data engineering.

• Extracted and processed data from multiple sources using LangChain, Python, and libraries like Beautiful Soup and Selenium, consolidating documents for comprehensive corpus analysis within Python-developed processes.• Segmented the corpus into manageable chunks and converted text into embeddings using cutting-edge API methods for efficient retrieval, stored within a Pinecone vector database utilizing query-optimized schemas.• Implemented models leveraging GPT-4 within RAG frameworks to provide solutions based on user queries, directly improving data querying effectiveness through LangChain.• Developed solutions interface with Streamlit, creating user-friendly interaction designs interfacing machine learned conclusions with enterprise data strategy.• Dedicated efforts in pioneering new Cloud Technologies, involving Generative AI methods within processing pipelines, enriching financial analytic solutions across interdisciplinary ranges.

• Created & developed different RPA bots with solution diagrams, explanations, and high-level visual representations, collaborating closely with team members to ensure seamless integration and functionality.• Enhanced data quality and accessibility by 95% through efficient data collection and preprocessing strategies.• Implemented and architected ML services using Python and the Flask framework, ensuring robust and scalable solutions.• Utilized Scikit-learn, TensorFlow, PyTorch, and Keras to train machine learning and deep neural network models, successfully contributing to the development of over 5 high-impact models.• Implemented learning rate scheduling and early stopping techniques in Natural Language Processing models for Sentiment analysis, reducing training time by 20%.• Assessed model performance on validation and test datasets using metrics like accuracy, precision, recall, and F1-score, achieving an average accuracy of 90% across multiple projects.• Partnered with cross-functional teams to translate business requirements into AI/ML solutions, effectively communicating complex technical concepts to non-technical stakeholders.• Implemented Proof of Concepts (PoCs) based on client requirements using Python and machine learning algorithms to demonstrate the feasibility and value of AI/ML solutions.

• Implemented foundational Machine Learning algorithms and ensured data quality and accessibility for effective model development.• Analyzed and debugged Machine Learning code demonstrated proficiency in resolving bugs and contributed to the development of new programs.• Conducted in-depth EDA (Exploratory Data Analysis) on large datasets, leading to the creation of visually impactful Tableau dashboards that provided key insights for decision-making in building models.• Actively participated in code reviews and followed best practices in Artificial Intelligence (AI) development to enhance model performance and reliability.• Provided reliable support in troubleshooting and debugging user issues, ensuring the stability and performance of AI solutions.• Utilized AI frameworks such as Scikit-learn and NLTK (Natural Language Toolkit ) to develop and implement effective machine learning models.