Results-driven Machine Learning Engineer with a passion for leveraging cutting-edge technologies to create innovative solutions. Demonstrated ability to collaborate effectively within cross-functional teams and deliver h…
Results-driven Machine Learning Engineer with a passion for leveraging cutting-edge technologies to create innovative solutions. Demonstrated ability to collaborate effectively within cross-functional teams and deliver high-quality projects within deadlines. Strong problem-solving skills and a proactive approach to tackling complex technical challenges. Committed to continuous learning and staying updated with the latest industry trends and advancements.
As a Data Scientist at Capgemini for the past two years, Ive spearheaded numerous projects leveraging generative Al techniques to drive innovation and solve complex problems. My responsibilities included data analysis, model development, and collaborating with cross- functional teams to deliver actionable insights and solutions. I successfully completed several generative Al projects from conception to deployment, resulting in serving thousands of users. Additionally, continuously sought opportunities to enhance our methodologies and processes, staying abreast of the latest advancements in Al and data science to ensure our solutions remained cutting-edge and impactful.
Developed a sophisticated Generative AI project focused on creating an AI-powered chatbot capable of promptly responding to user queries.
Leveraged a vast repository of client document files (8000+ files) for Retrieval Augmented Generation (RAG), enhancing the chatbot's knowledge base and significantly improving response accuracy.
Implemented Azure Cognitive Search and established a MongoDB vector store to streamline indexing and retrieval processes, ensuring efficient access to pertinent information within the document corpus.
Engineered a robust Data Pipeline using Azure Data Factory (ADF) to seamlessly transfer data from Azure Blob Storage to MongoDB database collections, optimizing data flow and storage efficiency.
Orchestrated the storage of user conversation data on MongoDB, providing users with session history for seamless continuation and reference, ensuring a personalized experience.
Achieved remarkable efficiency, with the chatbot consistently responding to user queries regarding documents within a maximum timeframe of 8-9 seconds.
Deployed the chatbot utilizing FastAPI on Azure Web Services with Docker, ensuring scalability, flexibility, and smooth deployment in a production environment.
Demonstrated exceptional problem-solving abilities, adaptability, and effective communication, culminating in the successful delivery of the project within the stipulated timeline.
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