Expertise in building end-to-end Machine Learning, NLP, CV based Solutions, Architectures, and Products. Essentially solves business problems using data, machine learning, analytical/relevant scientific experimentation by most effectively infusing the industry/domain expert's knowledge into the solution thereby delivering transformative AI use-cases that bring significant impact to the organizations in terms of growth, costs and operational efficiency for real. I perform critical assessment of business objectives in order to formulate as feasible ML problems. Then I devise the solution and lead the design, architect and build overall ML system that works in production successfully delivering business value. 8+ years into devising, building and implementing Deep Learning, Machine Learning, Statistical Modeling based solutions alongside various advanced analytics solutions catering to multitude of business problems using Python (Pandas, SciPy, Numpy, scikit-learn), Tensorflow, Pytorch, Flask, FastAPI, SQL, Graph technologies like Neo4j, AWS Neptune, GNNs, big data technologies/environments - Spark, Hadoop eco-system, Hive; GCP, AWS and Azure cloud platforms. Rigorous experiences of solving various kinds of end-to-end AI/ML/analytics use cases. Some of the deep learning / AI use cases Automation using Intelligent Document Processing and Knowldge Model based solution, Document digitization and extraction, Document Classification, Image Classification, Object Detection, Semantic Segmentation etc. Have been experimenting with GenAI solutions and systems using LLMs from Huggingface using Huggingface Transformers, LangChain, FAISS, ChromaDB etc. Competent in Machine Learning System Design, Test Driven MLOps Development, Tools, Methods and Best Practices for deployment and Production in on-prem, cloud, and hybrid environments. Adept with algorithms like CNN, DNN; NLP techniques, Named Entity Recognition and Linking, Relationship Extraction, LSTMs, Transformer based architectures, variants of BERT, Graph Neural Networks, Node Classification, Link Predictions, Graph Embeddings, Designing and Developing custom Knowledge Graphs etc; Traditional analytics use cases like Risk-score modeling, Customer Segmentation (Unsupervised and Semi-Supervised Learning based), Look-alike modeling, Time series modelling and forecasting, cross-sell, up-sell modeling, loyalty analytics, A/B testing, clustering methods, linear / quadratic integer programming, mathematical formulation of the optimization problems, implementing Genetic Algorithms etc.