Responsibilities include:

• Spearheaded the development and optimization of graph-based Retrieval-Augmented Generation (RAG) techniques for Large Language Models (LLMs), enhancing the accuracy and efficiency of information retrieval processes.
• Engineered and integrated Neo4J knowledge graphs with LLMs, enabling advanced semantic understanding and context-aware responses in generative AI applications.
• Designed and implemented hybrid LLM architectures, combining diverse AI models to leverage their strengths and improve overall system performance.
• Led research initiatives in generative AI, contributing to cutting-edge development in natural language processing and machine learning.
• Collaborated with cross-functional teams to translate complex technical concepts into actionable solutions, driving impactful outcomes for various AI-driven projects.

Responsibilities include:

• Addressed complex business problems through Statistical Learning methods that include deep learning based computer vision and text analytics techniques, end-to-end Machine Learning workflow design, data wrangling, data cleaning, feature engineering, development of robust Supervised and Unsupervised Machine Learning algorithms, optimization and deployment of predictive models as Flask APIs or in docker.
• Worked extensively on clustering, classification, regression, predictive modeling on big data using Spark (Scala and Python - PySpark), multivariate analysis, statistical analysis using IoT, LTE, 4G and 5G data. Expert in 5G core network domain, 5G Service Based Architecture, IoT, Edge and Fog computing .
• Drones capture movement of traffic images at different locations of a city and images are sent in stream to edge servers.
• Developed CNN based Deep Neural Network to detect high traffic at edge servers. Consolidated result is sent to Cloud to take further decisions and alarm generation.
• Developed parser for extracting user events from moderate to massive, semi-structured text data coming from highly distributed, third party network adaptors.
• Developed NLP and Machine Learning algorithms for analyzing and predicting performance degradation from user event data. Deployed the model in production.
• Developed Machine Learning models to predict SLA breach of support tickets that reduced actual SLA breach by 35%.
• Developed nine patent-pending solutions as individual inventor.
• End-to- end designing and implementation of statistical modelling, advanced data science solutions and products from acquiring business needs to final delivery, developing predictive models for field service operations for major telecom operators, predicting faults in LTE networks and new product demand forecasting.