Feb 2025 – Oct 2025 · 8 mos

Feb 2025 – Oct 2025 · 8 mos

• Deployed LLMs using Ollama and LangChain for natural language querying of business data.
• Applied prompt engineering and RAG (Retrieval-Augmented Generation) for context-aware, accurate responses.
• Integrated Whisper and SpeechRecognition API for voice-to-text functionality.
• Utilized Python, FastAPI, and Pandas to process queries and retrieve real-time KPIs from internal databases.
• Delivered a conversational interface enabling voice/text-based access to key business insights.

Jul 2021 – Aug 2024 · 3 yrs 1 mo

• SEEQ – Condensate return (Data Scientist)
• SEEQ Tool - Energy use case
• Collaborated with engineering teams to model steam flow using relevant tags, ensuring accurate data representation
• Employed SEEQ Workbench to visualize and analyze steam production data, identifying trends and patterns for process improvement.
• Conducted tag modeling for steam generation equipment, ensuring accurate representation of process variables.
• Utilized statistical analysis techniques to assess condensate return performance and predict future trends.
• GenAI Chatbot for Enhanced Customer Experience
• Role: Data Scientist
• Technologies: Open API, NLP, Machine Learning, Data Embedding, Microsoft Azure
• Project Overview: Developed a generative AI chatbot designed to enhance customer interactions by providing accurate and immediate responses to queries.
• Key Responsibilities:
• Data Preparation: Collected and pre-processed customer interaction data, dividing it into manageable chunks for efficient processing.
• Embedding and Storage: Utilized an open model to create embeddings of the data and stored these embeddings in vector format to facilitate rapid information retrieval.
• Chatbot Implementation: Integrated the AI model with Open API, enabling the chatbot to understand customer queries and generate precise responses based on the stored vector data.
• Result Optimization: Continuously refined the model to ensure that the chatbot provided the most relevant and helpful responses to customer inquiries.
• Impact: Improved customer satisfaction by significantly reducing response times and increasing the accuracy of information provided

Jul 2021 – Jun 2024 · 2 yrs 11 mos

Dec 2020 – Jun 2021 · 6 mos

• Internship
• Share Market NLP Project – Finance Domain
• Developed an NLP solution to extract stock tickers from share market data using regex, spaCy, and NER (Named Entity Recognition).
• Used MongoDB for efficient data storage and retrieval of financial records.
• Created RESTful APIs with Flask to serve processed data for real-time consumption.
• Applied text processing techniques on unstructured financial documents for improved market analysis.
• Contributed to data-driven decision-making in the finance domain using scalable Python-based pipelines.

Jun 2020 – Aug 2021 · 1 yr 2 mos

• One of the quality projects we delivered is Product Description Generator Using NLP

May 2020 – Oct 2020 · 5 mos · India

• Data Quality Assessment
• Data Insights
• Data Insights and Presentation