• Defined goals and execute the AI roadmap for product development • Successfully defended proposal to be part of NVIDIA inception program for AI & computer vision based product development• Spearheaded research and development efforts in Generative AI, leading to the creation of innovative products• Collaborated closely with product teams to integrate generative models into real-world applications, driving product deployment and user engagement• Implemented integrated data analysis pipeline for detecting real time anomalous signals using AWS IoT devices• Architected computer vision (CV) pipeline for detecting objects and integrated realtime observation of function and safety system(RTOFS)• Developed CV based depth estimation application as a product in designing facades• Creating internal knowledge and discovery search with LLM’s(large language Models)

✎ Operational :• Implementing cloud based infrastructure for startups and accelerate PoC’s for clientsaccording to the requirements• Provided client side support for consolidating data silos across enterprise into a data-lake for building dashboards and predictive analytics platform• Communicating with cross-functional agile teams involving data scientists, analysts,developers and business unit key decision makers across continents (US and UK)• As a product owner developing product backlog and communicating with stake holders• Contributing to evaluation of project requirements, resource allocation and risk analysis• Data driven prioritisation and enhancement of product features and backlogs design• Leverage my cross disciplinary skills in building and evaluating projects✎ Analytics and ML:• Developing end to end machine learning prototypes according to clients specifications• Deriving actionable insights from large databases and data lakes• Working with patient electronic health records for predictive analysis• Implemented time series forecasting of sales targets, AP/AR using LSTM (deep learning)and PROPHET• Cloud based analytics and machine learning solutions on AWS and Azure• Overseeing end to end pipelines for SaaS (software as service) and Infrastructure asCode)• Building graph database with clinical longitudinal data (Electronic Health Records) andevaluation using graph data science (Stratification/Similarity and community detection)

✎ Multi-language, multi-label document classification (advAIsor)- Built a natural language processing(NLP) pipeline with Swiss parliament data for - multi language detection- topicmodellin,- named entity recognition(NER)✎ Prediction of peptide iRT (retention time) in clinical mass spectrometric data utilising machine learning and deep learning models