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Profile

Experienced Data Scientist with a demonstrated history of working in IT industry. Collected data and drew insights to improve business operations and solve business problems.

Personal Projects
WeldCraft, Hybrid Search, RAG
Apr 2024 – Apr 2024
  • •Deployed Chroma (an open-source vector database) on EC2 with a security layer.
  • •Developed a hybrid search (keyword search + semantic search) algorithm for pdfs and web page using langchain
  • • Built RAG chatbot with history for these pdfs and web pages using langchain and openai(gpt-3.5-turbo)
  • •Deployed hybrid search and RAG chatbot as Fastapi endpoint and nginx
  • •Designed a webapp for accessing these endpoints using nextjs, tailwindcss.
  • •Built CI/CD pipeline using GitHub actions and ec2 as self-hosted runner.

    • Tech: Langchain, Vector databse, RAG, Openai, llm, github actions, ec2

    Nov 2023 – present
  • •Designed and implemented a dynamic document-based Q&A platform, enabling users to upload diverse file formats (PDFs, Excel, JSON, text) for intelligent querying.
  • •Integrated OpenAI and LangChain for advanced natural language processing, while securely storing documents on AWS S3.
  • •Implemented Clerk for robust authentication, Pinecone for efficient document indexing, and PostgreSQL (Neon DB) for seamless chat and message history storage.
  • •Additionally, incorporated Stripe for streamlined payment processes, enhancing the overall user experience.
  • •Crafted a responsive and intuitive front-end experience using Next.js, TypeScript, and Tailwind CSS, complemented by a robust Node.js backend. Implemented cutting-edge technologies to ensure a seamless user interface, blending the power of Next.js for efficient rendering, TypeScript for type safety, and Tailwind CSS for a sleek and modern design. The result is a dynamic and user-friendly platform that enhances the overall usability and accessibility of the application.
  • •Deployed the webapp on vercel.

    • Tech: OpanAI, RAG, Langchain, Pinecone, AWS S3, NextJs, Typescript, Tailwindcss

  • •Aim was to extract questionnaire from given text and Json input without using spacy.
  • •Developed a robust solution to extract questionnaires from unstructured text and JSON inputs using advanced NLP techniques.
  • •Conducted thorough preprocessing of both text and JSON data to ensure optimal performance of the models.
  • •Implemented two classification models:
  • •XGBoost classifier for JSON data processing.
  • •LSTM for text data analysis.
  • •Employed fine-tuning techniques on BERT for Named Entity Recognition (NER) to eliminate irrelevant information from the output of classification models.
  • •Achieved enhanced accuracy and efficiency in questionnaire extraction through iterative model training and refinement.

    • Tech: XGBoost, LSTM, Pytorch, TensorFlow, HuggingFace

  • •Built a CNN to classify plant seedling images into 10 classes.
  • •Performed data augmentation with the help of Image data generator, Achieved 90% accuracy.

    • Tech: CNN, Data Augmentation, TensorFlow, Adam, Relu, SoftMax

  • •Aim was to classify the Accident Severity based on vehicle type, driver’s age, sex and 28 more features in three classes as slight, serious and fatal.
  • •Most of the accidents with fatal injuries happened on weekends between 2pm to 7pm. Received 90% weighted f1 score over tuned ExtraTree classifier. Implemented Explainable AI with SHAP and deployed it on Heroku cloud.

    • Tech: Oversampling (SMOTE), Recall, Precision, weighted f1, ExtraTree class., Streamlit

  • •Implemented regressor models to reduce the energy consumption of buildings by a combination of easy-to-implement fixes and state-of-the-art strategies which is beneficial to reduce 37% of global energy-related and process-related CO2 emissions in 2020 during the lifecycle of buildings from construction to demolition.
  • •Features like floor area, energy star rating are affecting the EUI and there is no relationship can be observed in EUI and the weather-related numerical columns. Performed explainable AI with SHAP.

    • Tech: KNN Imputer, Label Encoder, Cross-Validation, Catboost and RF regressor, Optuna

    Skills
    Machine Learning — Classification, Regression, clustering, Decision Trees, K-Means Clustering, hierarchical clustering, Deep Learning — DNN, CNN, RNN, Transfer learning, LSTM, LLM, GenAI, RAG, Statistical Methods — Predictive analysis, Hypothesis Testing and Confidence Intervals, Principal Component Analysis, LDA and Dimensionality Reduction, Programming Languages and tools — Python, Java, HTML and CSS, Version Control Tools — Git, Python Libraries — Sklearn, Scipy, Statsmodel, Pandas, NumPy, Seaborn, Matplotlib, Selenium, BS4, NLTK, TensorFlow, Keras, pytorch, langchain, openai, hugginface, shap, Boto3, Flask, Fastapi, Streamlit, Scripting Language — Unix, Database Language — SQL, Data Reporting Tool — Excel, GCP Data Studio, Cloud Tools — AWS (sagemaker, s3, Lex, Lamda, Bedrock, RDS, EC2, API gateway, secret manager), GCP (Bigquery,  | Dataproc, Dataflow, Vertex AI, cloud SQL, etc.)
    Professional Experience
    Dec 2022 – Aug 2024 | Remote, India
  • •Bank Statement Analyser(BSA):
  • •Using computer vision techniques and algorithms such as image processing, pattern recognition, and optical character recognition (OCR), I developed a solution that improved the accuracy of detecting transactions and other details in bank statements by over 95%. (API link)
  • •The implementation of the computer vision solution had a significant impact on the organization, reducing manual labor, improving accuracy, and saving time.
  • •Deployed and managed a RESTful API on a Google Cloud Platform using Docker for 20+ countries, ensuring scalability, security, and optimized performance.
  • •Collaborate closely with clients to understand their unique objectives, translating their requirements into actionable steps for the implementation of our machine learning solution, leading to highly customized and effective deployments.
  • •Bank Statement Extraction(BSE):
  • •Built an API for extracting basic details like account holder name, A/C number, statement duration, etc.
  • •Used OpenAI GPT model to get these basic details from raw OCR output.
  • •Successfully deployed the API on Google Cloud Platform (GCP), ensuring scalability and reliability for seamless integration with the application's backend infrastructure.
  • •Intelligent Document Retrieval and Chatbot System:
  • •Document Processing:
  • •Implemented efficient chunking logic to partition documents into manageable segments for streamlined processing.
  • •Conducted rigorous testing of chunking efficiency, fine-tuning parameters to optimize performance.
  • •Model Deployment and Integration:
  • •Selected and deployed embedding and Large Language Model (LLM) models compatible with AWS Bedrock, ensuring seamless integration with the system architecture.
  • •Configured and deployed ChromaDB on AWS as a client-server, establishing Lambda triggers for real-time document updates in S3 and efficient embedding addition/update operations
  • •Document Access Control and Chat History Management:
  • •Designed and implemented a schema for storing chat history in a relational database (RDS), capturing and storing each conversation with precision.
  • •Chatbot Pipeline Development:
  • •Developed a sophisticated pipeline to process user questions, user IDs/usernames, and chat history seamlessly.
  • •Integrated logic to determine document access based on user ID, delivering tailored responses accordingly.
  • •Monitoring and Validation:
  • •Established CloudWatch metrics for monitoring Lambda calls and outputs, ensuring system reliability and performance.
  • •Conducted extensive validation and testing of the end-to-end pipeline, crafting benchmark questions and meticulously assessing retrieval and response accuracy.
  • •Documented testing outcomes comprehensively, iteratively refining the system for enhanced performance.
  • •API Gateway Setup:
  • •Orchestrated the setup of Lambda functions and API gateway to facilitate seamless API interactions, enabling effortless integration with external systems.
    • Oct 2021 – Dec 2022 | Pune, India
  • •Developed a machine learning model using Python and scikit-learn library that predicted the likelihood of insurance claims being fraudulent based on claimant information and historical claims data.
  • •Evaluated the model's performance using metrics such as precision, recall, and F1-score, and presented the results to stakeholders.
  • •The project resulted in a 20% reduction in fraudulent claims, saving the insurance company millions of dollars in losses.
  • •Azure cloud migration- Migrating on-prem (ASIS) to Azure cloud (ToBe)
    • Dec 2018 – Oct 2021 | Pune, India
  • •Getting alerts insights using pandas and NumPy- Scraping the data using selenium and BS4 to get the raw data from alert reporting tools such as WebGui and analyzing this data to avoid unnecessary failures
  • •Primary Bigdata and ETL support- Monitoring Informatica, Abinitio and Bigdata jobs through TWS, SOA and Informatica
  • •Triggering automatic emails to client for alerting visa batch start and end by reading live SVG graphs/ pictures from website using python
  • •Generating daily reports such as Bigdata (Hound cluster and Omega cluster) job completion using TWS scrapped data with selenium and BS4
    • Certificates
    Education
    Jul 2014 – Jul 2018