Al-driven Data Scientist experienced in designing, developing, and deploying end-to-end Machine Learning solutions, specializing in LLMs and foundation models with IBM Watsonx and GCP Vertex Al. Proficient in Python and key Deep Learning frameworks (PyTorch, TensorFlow, Hugging Face), I translate business process requirements into impactful Al applications. Proven in full project lifecycle management and effective stakeholder communication, committed to continuous learning in Generative Al.
Machine Learning Intern
Internship Studio
Dec 2025 - Present
Designed, implemented, and refined NLP and Deep Learning models (TensorFlow/PyTorch) for chatbot applications, focusing on enhancing intent recognition, response generation, and overall, Al solution performance. Collaborated with cross-functional product teams and engaged with stakeholders, leveraging strong communication skills to deploy conversational Al solutions using Flask, manage software configuration, version control (Git), and apply customization for business requirements. Contributed to the full Al project lifecycle, including model validation and pre-deployment testing phases, ensuring robust performance and aligning Al solution deployments with business process designs and project timelines. Proactively resolved configuration, model integration, and deployment challenges through problem-solving discussions and clear technical communication, ensuring successful software validation stages.
Statistics
Pragati Mahavidyalaya Degree College, India
7.53
Python 101 for Data Science
IBM
Generative AI with Gemini API & Vertex AI
Google Cloud
AI - Image Captioning System
Designed and implemented an end-to-end Deep Learning solution for image captioning, integrating ResNet-50 (for vision) and LSTM with Attention mechanisms (for language) to achieve robust vision-language alignment. Optimized model performance significantly through transfer learning and quantization techniques, successfully reducing inference time to 200ms, crucial for real-time Al solution deployment. Deployed the captioning model as a scalable "As-a-Service" solution using Flask, demonstrating proficiency in model deployment and full-lifecycle project management. (Considered deployment scalability for potential Kubernetes). Applied advanced predictive modelling techniques for dynamic caption generation, improving both accuracy and contextual relevance to support informed decision-making tasks with the generated NLP outputs.
AI Shopping Assistant
Designed and developed a cloud-based NLP assistant that transforms vague product inquiries into actionable search queries using LLMs (Llama 3 via IBM Watsonx) and advanced Machine Learning algorithms for initial prototyping designs. Applied LangChain and IBM Watsonx to implement real-time query interpretation, text analytics for intent handling, and HTML summarization, demonstrating a strong understanding of foundation model application in practical scenarios Integrated third-party APIs (SerpAPI) and maintained structured project repositories using Git or GitHub, making the project available as an open-source reference where applicable. Customized solution workflows based on business requirements, contributing to the improvement of cloud-based Al deployment processes, including iterative testing and refinement of the NLP components.
Cloud & Deployment
Soft Skills
Programming
Frameworks
AI/ML Libraries
AI/ML Concepts