Software Developer Senior
Resume:
SUCHITRA HOLE
****************@*****.***
Boston, MA
linkedin.com/in/suchitra-hole
github.com/Suchi1998hole
My Portfolio
PROFESSIONAL EXPERIENCE
LTIMindtree, Pune, India: Senior Software Developer Jan 2021 – July 2024
• Designed and deployed high-performance Python-based APIs on Azure Functions, leveraging asynchronous ex- ecution and serverless architecture to optimize cloud resource utilization. Implemented caching mechanisms and parallel processing to handle requests efficiently resulting in a 40% reduction in deployment costs and enhancing scalability while reducing cold start times for API calls.
• Developed and optimized Flask REST APIs to efficiently extract, transform, and analyze structured and semi- structured data from multiple sources, including CSV, XLS, and MSSQL databases. Implemented data validation, preprocessing pipelines, and error handling mechanisms to ensure data integrity and consistency.
• Engineered 24 ETL pipelines and designed 6 schedulers for automated bulk file processing, leveraging Python, Apache Airflow, and python libraries to streamline data ingestion. Integrated cloud-based storage solutions for distributed data management.
• Automated report generation workflows using Python, SQL stored procedures and Power Automate, drastically reducing manual intervention from 4 hours per week to just 30 minutes. Built custom data visualization dash- boards for dynamic reporting and integrated scheduled report generation via email automation (SMTP), Tableau dashboards.
• Designed high-performance Mulesoft APIs using RAML, DataWeave 2.0, and Mule Runtime Fabric, reducing data retrieval time.
• Developed event-driven solutions on Runtime Fabric Cloud, streamlining deployment and reducing overhead. Eyeot Tech Solutions, Pune, India: Technical Lead Aug 2020 - Dec 2020
• Architected an IoT-based home intrusion detection system using PIR and sound sensors with MicroPython on ESP32, MQTT, Firebase for cloud logging, and Twilio (API) for real-time alerts.
• Developed a React Native-SQL-Firebase application with complex back-end logic featuring real-time data syn- chronization for a smart mirror.
• Engineered an LLM-powered voice recognition system using Python, integrating Transformers, BERT, and MQTT to enable real-time voice command execution, achieving 82% command accuracy for improving user-mirror inter- action.
• Project Management and Client Relations: Involved in complete product life cycle from requirements gathering to end-of-system testing and deployment. Interacted with clients for requirement gathering and project planning. Tech Smart Systems, Pune, India: Data Science Intern Jun 2019 - Dec 2019
• Applied a diverse range of ML algorithms including XGBoost, Random Forest, SVM, and KNN for classification and regression tasks across multiple datasets from UCI Machine Learning Repository, achieving F1-scores ranging from 0.88 to 0.95 and R scores up to 0.92.
• Implemented Convolutional Neural Networks (CNNs) for image classification, utilizing techniques like data aug- mentation, batch normalization, and dropout to enhance model generalization. Achieved highest accuracy on benchmark datasets after 100 epochs of training.
• Developed Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models for time-series forecasting, leveraging sequence-to-sequence learning and attention mechanisms to improve prediction accuracy.
• Applied Bayesian optimization for hyperparameter tuning of XGBoost, reducing classification error through iterative model refinement.
• Created insightful visualisations using Matplotlib and Seaborn to illustrate feature importance, model performance, and data distributions, aiding in stakeholder communication. EDUCATION
Master’s in Data Science Sept 2024 - Present
Northeastern University, Boston, MA
Khoury College of Computer Science
Relevant coursework: Supervised Machine Learning, Unsupervised Machine Learning, NLP, Algorithms Bachelor’s, Computer Engineering Aug 2016 - April 2020 Savitribai Phule Pune University, Pune, India
Dr. D. Y. Patil School of Engineering
Relevant coursework: Data Mining and warehousing, DBMS, OOP, Computer Architecture TECHNICAL SKILLS
Programming Languages: SQL, Python, Java, C++, C, Go, Java Script (React.js, Next.js, Angular.js) Big Data Analytics: Power Bi, Tableau, Looker, PySpark, Apache Kafka, Snowflake, Hadoop ML Libraries: NumPy, Pandas, Seaborn, Matplotlib, Keras, PyTorch, SciPy, Scikit-learn, spaCy, TensorFlow, Gensim, NLTK, Pillow, Scikit-Image, OpenCV
Cloud & DevOps Tools: AWS, Azure, Jenkins, Docker, Kubernetes Tools & Frameworks: PostgreSQL, MongoDB, MuleSoft, Eclipse, Spring Boot, Oracle, Git, VS Code, Maven, JUnit, SonarQube, Postman, Android Studio, Anaconda
CERTIFICATIONS
• Quantum Computing and Machine Learning - IIT Delhi (Nov 2024)
• Mulesoft Certified Developer - Mulesoft (July 2023)
• Data Science with Python - Simpli Learn (Sept 2022) PUBLICATION
A Survey on Detection of Inorganic Substances in Vegetables and Fruits Published in the International Journal of Scientific Research in Computer Science, Engineering and Information Tech- nology (Volume 4, Issue 8, November 2019)
LEADERSHIP & ACHIEVEMENTS
Advanced Machine Learning Seminar
Organized and conducted a comprehensive two-day workshop at D.Y. Patil College of Engineering, focusing on cutting- edge topics such as Generative Adversarial Networks (GANs) and Reinforcement Learning. Drone assembly and coding
Led a team of 4 at IIT Bombay Techfest, successfully assembling and piloting a quadcopter drone with enhanced capabilities. The drone featured a Pixhawk controller and GPS module, enabling stability, position hold, and return-to- home functionalities.
PROJECTS
AI-Powered Research Assistant with RAG and DeepSeek R1 7B Developed research assistant using Retrieval-Augmented Generation (RAG) with DeepSeek R1 7B, enabling intelligent critique and retrieval of research papers from arXiv. Implemented keyword extraction, vector embeddings, and a custom knowledge base for semantic search and contextual awareness, reducing AI hallucinations and improving retrieval precision.
Reinforcement Learning-based Arbitrage Trading Model Developed the model using Deep Q-Learning, integrating rolling spread & volatility features, adaptive epsilon decay, reward smoothing (EMA), and a stop-loss mechanism for optimized trade decisions between cryptos: Binance and Coinbase.
Stock Prediction Algorithm
Implemented an LSTM neural network to forecast stock prices, specifically for Google (GOOG). Utilized TensorFlow, Keras, and yfinance to preprocess data, train the model, and evaluate predictions, demonstrating expertise in time series forecasting.
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