Sunit Gaikwad

Results-driven Machine Learning Engineer with 4 years of experience in developing and implementing innovative ML solutions, particularly in NLP and predictive modeling. Demonstrates expertise in Python and various cloud platforms including AWS, GCP, and Azure, utilizing frameworks like TensorFlow and PyTorch to deploy scalable applications. Capable of conducting thorough exploratory data analysis and leveraging Generative AI to enhance operational efficiency. Committed to delivering actionable insights through advanced data visualization techniques.

News Insight Extraction with AnswerNet

Organization: 47Billion

Responsibilities:

· Developed an AIresearch assistant for finance, leveraging Generative AIalongside Bright Data, Lang-chain, OpenAI, and Streamlitfor optimized information retrieval.

· Designed a user-friendly Streamlit UI, integrating Generative AIcapabilities for insightful financial article analysis and enhanceduser experience.

·  Ensured response accuracy by combining article content with Generative AI models tailored for finance-related queries.

· Emphasized continuous improvementthrough user feedback, testing, and Generative AI-driven enhancements.

Biomedical Entity Extraction with Deep Learning

Organization: 47Billion

Responsibilities:

· Mastered Biomedical Entity Extraction with Deep Learning by building a custom Transformer model, ensuring precise entity recognition across diverse document formats (PDFs, TXT, CSV) using PyPDF2 and NLP expertise.

· Applied Word2Vec and PyMuPDF to generate semantic representations, implementing AI-powered annotation for automatic identification and tagging of entities within PDFs.

· Implemented robust data preprocessing pipelines to achieve a 95% reduction in data errors and enhance overall data quality.

· Successfully detected and addressed outliers, minimizing their impact by 90% and improving data accuracy.

· Collaborated closely with domain experts and data scientists to identify and select  pertinent features,thereby contributing to a notable increase in the predictive accuracyof the models.

SmartCare: Proactive Maintenance System using Machine Learning

Organization: 47Billion

Responsibilities:

· Collect datafrom sensors, control systems, and other sources related to industrial equipment. Work with domain experts to identify relevant parameters such as temperature, pressure, vibration, and operational conditions.

· Cleanse the collected data by handling missing values, outliers, and inconsistencies. Apply techniques such as normalization, feature scaling, and dimensionality reduction to prepare the data for modeling.

· Utilize statistical methods, exploratory data analysis, and domain knowledge to select and prioritize features for modeling.

· Evaluate and choose appropriate machine learning algorithms for predictive maintenance tasks. Train machine learning models using various algorithms and techniques. Tunehyperparameters using cross-validation and grid search techniquesto optimize model performance.