Curriculum Vitae

• Specialization in Generative AI, LLMOps, Bioinformatics, and Cheminformatics
• Master Thesis: "Leveraging LLMs for Enhanced Drug Discovery: Extracting Insights from Patents"

• Focus on AI/ML applications and software systems
• Minor in Data Science

• Developing RAGs and AI pipelines in Drug Discovery
• Leveraging Knowledge Graphs information for drug repurposing and repositioning with classical ML and AI models
• Research on using LLMs and Neural Networks on Knowledge Graphs for drug discovery

• LLM Training and Fine Tuning (Mistral, Llama)
• Machine Learning Optimization and Integration for business use-cases
• Cloud Services and Infrastructure Management (Microsoft Azure, GCP, AWS, Terraform)
• MLOps & LLMOps - monitoring and deployment of ML and LLM models (Azure AutoML, Azure OpenAI Service)
• Containerization and Orchestration (Docker, Kubernetes)

• Docker and Kubernetes Cluster Management for Service Deployment and Scaling
• Web and RESTful API Development (Python Tornado, Flask, Django)
• AI Model Integration and Deployment (Diffusion Models, GPTs) with Kubernetes and Containerization
• Rigorous Testing and Monitoring for AI Model Reliability and High Availability

• Data Analysis & Expressive and Insights driven Data Visualizations using Pandas & Matplotlib
• Training and Testing Machine Learning & Deep Learning Models using Scikit-Learn, XGBoost, LightGBM, Keras, TensorFlow
• Development of Fulfillment Based Healthcare Chatbot with RASA Framework
• API Development & Deployment with FastAPI and Flask Frameworks
• Development of Test Driven ML UI with Streamlit
• Deployment, Regular Update & Monitoring of ML Models on GCP & Amazon EC2 Platforms

• Comment classification on Twitter Livestream data with Word Vector Embeddings (word2vec, BERT)
• Development of Interactive web-app for real-time toxic words classification from sentences
• Deployment and monitoring of ML across Google Cloud Platform

• Data Visualization and Analysis using Tableau and PowerBI
• Data Curation, EDA, Analysis and Insights generation on Plant Disease Data with SVM, PCA, Bagging (Ensemble Learning models) and Boosting (XGBoost, CatBoost & LightGBM)

A sophisticated implementation of RASA Conversational AI on a HealthCare Bot integrated with a Web and Android Software written in Python, Java and JavaScript.