Sivasanthosh Vempali

• Checked design compliance with product specifications and standards requirements
• Build prototypes to explore new scenarios and optimizations of existing solutions
• Performed exploratory data analysis using Pandas, Numpy and Matplotlib libraries in Python.
• Developed automated reports using Python libraries such as PyPDF2.
• Work with stakeholders to perform root-cause analysis & figure out solution approaches
• Evaluate and recommend technology and frameworks for flexibility, long-term viability, and time to market
• Assist in ramping up team, identifying tasks, estimating and planning work effort, tracking milestone completion, testing and all other project life cycle milestones
• Preparing reports on project specifications Analyzing information to recommend the installation of modifications of an existing system
• Consulting with team to evaluate software interfaces and develop specifications
• Work with development and customer success team from diverse technical backgrounds
• Developing software systems using scientific analysis
• Developed Python scripts to automate data extraction from various sources, such as databases and web APIs.

PROJECTS

• Cloud Migration - Backend Development
  Skills: Kubernetes, Docker, django, Python, MongoDB,
  Microservices, Node.js, Express.js, Pandas
  Project Overview:
  The Cloud Resources Cost Analyzer application is a web-
  based tool designed to streamline the process of selecting
  the optimal Virtual Machine based on user-specified
  criteria.The primary goal is to help organizations make
  informed decisions to minimize costs during cloud migration
  Roles and Responsibilities:
  Recommendation Engine: Developed APIs in Flask to
  recommend virtual machines based on user inputs, providing
  users with tailored suggestions to meet their specific
  requirements.
  Data Cleaning and Analysis: Utilized Python and Pandas for
  data cleaning and analysis, ensuring the accuracy and
  reliability of the input data crucial for effective
  recommendations.
  Microservices Architecture: Successfully converted the
  monolithic application to a microservices architecture in
  Python. This transformation enhanced scalability,
  maintainability, and overall system performance.
• Loan Predictor - Backend Development and Data
  Analysis
  Skills: Kubernetes, Docker, Flask, Python, MongoDB,
  Microservices, IBM Watson, XGBoost, LLM, Pandas
  Project Overview:
  The Loan Predictor application is tailored for small finance
  banks and institutions, streamlining the loan approval
  process by automating workflows and eliminating
  paperwork. Users can upload necessary documents and
  provide required information, after which the Loan Predictor
  app processes the data, facilitating a seamless and efficient
  loan approval process.
  Roles and Responsibilities:
  Data Cleaning and Analysis: Employed Python and Pandas
  for data cleaning and analysis, guaranteeing the reliability
  and accuracy of the data processed within the Loan Predictor
  application.
  Document Processing: Utilized LLM (Google Flan UL20 B) for
  document processing, extracting crucial information from
  uploaded documents to enhance the speed and accuracy of
  the loan approval process.
  Backend Development: Wrote and developed APIs and the
  entire backend using Python and Flask