AI Engineer and Data Scientist specializing in Generative AI, LLMs, and NLP. Expert in Python, SQL, RAG, AI Agents, and Langchain, with a proven track record of building intelligent solutions and translating technical insights into business value. Seeking to leverage expertise in AI and data science to drive innovation and impact.
• Developed and deployed AI solutions within an agile team, focusing on scalable machine learning models.
• Collaborated on optimizing NLP pipelines, reducing processing time by 15% using Python and TensorFlow.
• Conducted A/B testing on AI features, increasing adoption rates by 20% through data-driven refinements.
● Developed machine learning models using Python. Developed an AI-powered chatbot using LLMs and NLP.
●Gained proficiency in Python, Data science, machine learning, and GenAI, RAG, AI Agents, Langchain, LLM.
●Collaborated with teams and communicated technical concepts to non-technical stakeholders.
● Gained foundational expertise in data analysis, Data science, machine learning, Deep learning and GenAI.
● Developed expertise in data analysis, visualization, and analytics, enabling extract insights from data.
●Implemented data visualizations, built interactive tools, trained models, and deployed applications.
• Led the development of "Health Companion," an AI chatbot providing personalized health guidance using NLP and LLMs.
• Integrated Firestore and BigQuery for real-time user data and nutrition plans, enhancing user engagement by 30%.
• Designed context-aware conversational memory, improving user satisfaction scores by 25%.
• Developed an AI-driven chatbot to provide health guidance, integrating NLP and LLMs for query understanding.
• Leveraged Firestore and BigQuery for real-time data, enabling personalized health insights and nutrition plans.
• Improved user retention by 30% through adaptive responses tailored to individual health profiles.
● Developed an AI-powered chatbot for a coffee shop app using LLMs and NLP.
●Implemented an agent system for order-taking and personalized recommendations using a RAG system.
● Built a recommendation engine using market basket analysis.
● Integrated the chatbot into a React Native app for real-time interactions.
Technologies used: Python, React Native, LLMs, NLP, RAG, RunPod, Market Basket Analysis, Firebase.
● Built a model for heart disease risk using demographic and clinical data.
● Conducted data analysis, including biomarkers and medical history.
● Developed machine learning algorithms to stratify risk categories.
Technologies used: Python, Machine Learning, Data Analysis, Jupyter Notebook.
● Developed a Library Management System using SQL, table creation, CRUD operations, and advanced queries.
● Key features include database schema design, CTAS and complex SQL queries for effective data analysis.
NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, Keras
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Natural Language Processing (NLP), RAG, LangChain, LLM, AI Agents.
Cloud Platforms: Google Cloud (Vertex AI, BigQuery, Firestore), AWS, Azure
Logistic Regression, KNN, Random Forest, Decision Tree), Regression (Linear, Lasso, Ridge), Clustering.
SQL: MySQL, PowerBI, Python: Panda, Numpy, Seaborn,matplotlib.
Demonstrated expertise in designing and implementing AI solutions on Microsoft Azure.
Developed expertise in data analysis, visualization, and analytics, enabling the extraction of actionable insights from data.
Learned the basics of Retrieval-Augmented Generation (RAG), combining RAG techniques to AI models.
Relevant coursework: Machine Learning, Data Science, Artificial Intelligence, Database Management, Statistical Analysis.