Dec 2024 – Aug 2025 · 8 mos · Mumbai, Maharashtra, India · Remote

Jan 2023 – Nov 2024 · 1 yr 10 mos · Mumbai, Maharashtra, India · On-site

• At Reliance Jio, I've been instrumental in designing and implementing sophisticated backend services and machine learning models that are integral to our data-driven solutions. My work focuses on building resilient, scalable, and efficient systems that empower our AI-driven applications to deliver high-quality insights and predictions.
• Key Contributions:
• Data Preprocessing Service: Developed a robust data preprocessing pipeline that interfaces with MongoDB to fetch, clean, and transform raw text data into structured PDF documents. This service is a cornerstone for our Language Chain System, ensuring that data is standardized and optimized for accurate embedding and retrieval.
• Entity Extraction Service: Created a wrapper service that seamlessly integrates with our MLOps platform to extract entities from unstructured data. This service enhances our data processing capabilities, enabling more precise and contextually relevant outputs, which are crucial for various downstream applications.
• Alert Scoring Model: Engineered a machine learning model to predict the impact of alerts in complex IT systems. By incorporating factors like severity, environment, and alert rarity, I devised a rule-based weighted average approach that refines the impact percentage, allowing IT teams to prioritize responses effectively.
• Technical Skills:
• Proficient in Python and FastAPI for developing scalable backend services.
• Extensive experience with MongoDB, PostgreSQL, and Elasticsearch for efficient data storage and retrieval.
• Skilled in deploying machine learning models using scikit-learn, pandas, and imblearn.
• Expertise in integrating external APIs and handling asynchronous tasks with aiohttp and APScheduler.
• Strong background in data transformation and text processing using libraries like langchain and sentence-transformers.
• Through my contributions, I've helped drive significant improvements in system efficiency, data accuracy, and the overall user experience.

Jan 2023 – Jun 2023 · 5 mos · Remote

• As an intern, I have actively contributed to the development and optimization of cutting-edge REST API services and time series forecasting models. I developed and implemented robust solutions using Python, FastAPI, and SQLAlchemy, ensuring efficient data handling and seamless API integration.
• I built scalable containerized applications with Docker and Kubernetes, enabling smooth deployment and management in dynamic environments. My work also included designing machine learning algorithms, conducting thorough exploratory data analysis (EDA), and performing feature engineering to enhance model accuracy.
• In my role, I spearheaded efforts to refactor existing codebases, improving performance and maintainability, while systematizing processes for consistent and reliable outcomes. I analyzed data trends and delivered actionable insights, supervising the integration of these findings into production systems.
• This experience has provided me with a solid foundation in backend development and machine learning, allowing me to create and lead projects that drive impactful results.

Jun 2022 – Dec 2022 · 6 mos · Mumbai, Maharashtra, India · On-site

Mar 2022 – Jun 2022 · 3 mos · Remote

• As a research intern at IIT Guwahati, I worked on various projects that involved applying machine learning and data science techniques to real-world problems. Under the guidance of [Mentor's Name], I explored the applications of data science in healthcare, finance, and natural language processing.
• Key Responsibilities:
• Collected and preprocessed large datasets from various sources, including APIs, databases, and CSV files.
• Implemented and compared various machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques.
• Utilized data visualization techniques, such as Matplotlib, Seaborn, and Plotly, to communicate insights and findings.
• Collaborated with team members to design and develop predictive models, recommender systems, and natural language processing tools.
• Projects:
• 1. *Predicting Hospital Readmissions*: Developed a predictive model using logistic regression, decision trees, and random forests to predict hospital readmissions based on patient demographics, medical history, and treatment outcomes. Achieved an accuracy of 85% on the test dataset.
• 2. *Stock Market Sentiment Analysis*: Built a natural language processing tool using Python and NLTK to analyze stock market sentiments from news articles and social media posts. Achieved an accuracy of 90% in predicting stock price movements.
• 3. *Recommendation System for E-commerce*: Designed and developed a recommender system using collaborative filtering and matrix factorization to recommend products to customers based on their purchase history and preferences.
• Achievements:
• Published a research paper on "Predicting Hospital Readmissions using Machine Learning" in a reputable international journal.
• Presented a poster on "Stock Market Sentiment Analysis using Natural Language Processing" at a national conference.
• Collaborated with a team to develop a predictive model for disease diagnosis that achieved an accuracy of 95% on the test dataset.

Sep 2021 – Feb 2022 · 5 mos · Remote · Remote