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ddot-bit/README.md

Visitor

Diego Puducay

LinkedIn YourEmail@gmail.com


TECHNICAL SKILLS

  • Programming Languages: Python
  • AWS Services: Lambda, SQS, S3, CDK, EventBridge, DynamoDB, Step Functions
  • Web Frameworks: FastAPI
  • Version Control: Gitlab
  • Data Analysis: Pandas
  • Development Tools: Jupyter

PROFESSIONAL EXPERIENCE

PennyMac - Software Developer

June 2022 - Present

  • Contributed to a serverless backend application on Amazon Web Services using Python, boto3 SDK, AWS CDK, and Gitlab.
  • Collaborated in a Scrum team, implementing event-driven software architecture and designing/refactoring architectures using Lucidcharts.
  • Followed Agile Principles, including Daily Standups, Merge Parties, and Mobbing Sessions in 2-week sprints.
  • Utilized Jira, Confluence for documentation, Newrelic for KPI tracking, and Sonarcube for code coverage.

PennyMac - Internship: Software Developer

June 2022 - November 2022

  • Completed tickets successfully integrated into the proprietary code base.

Yosemite Community College District - Anthropology Lab Assistant

December 2018 - May 2019

  • Managed Microsoft Office tools, including Access, for maintaining a database of specimen data.
  • Conducted specimen measurements and preprocessing routines for cross-referencing source measurements.
  • Took on TA responsibilities, including alphabetization and compiling information into proprietary software.
  • Conducted extensive research on specimens for anthropology faculty classification.

EDUCATION

University of California, Berkeley
May 2022
Bachelor of Data Science

  • Emphasized model engineering, machine learning algorithms, optimization, and probability theory.

PROJECTS

Serverless Transformer Model

  • Deployed a pre-trained neural language model through AWS serverless platform using technologies such as AWS EFS, Lambda, API Gateway, S3, and DynamoDB.

Build Your Own World

  • Collaboratively constructed a two-dimensional world using boilerplate code on Github.
  • Designed classes to generate a world, landscape, and shapes while using an intermediate class to connect them.

Neural Language Models for Sentiment Analysis: Convolutional Neural Networks

  • Constructed a CNN for encoding contextual meanings via n-gram detection and dimensionality reduction.
  • Response variable is a distribution of binary labels over tokenized movie reviews.
  • Developed neural network architecture using PyTorch libraries and trained over GLoVE embeddings.

KEY ACHIEVEMENTS AND SKILLS

  • Successfully contributed to the development of serverless applications on AWS using Python and relevant technologies.
  • Applied Agile principles, including Daily Standups, Merge Parties, and Mobbing Sessions in a Scrum team environment.
  • Proficient in using Jira, Confluence, Newrelic, and Sonarcube for documentation, KPI tracking, and code coverage.
  • Completed a successful internship with hands-on experience in integrating tickets into a proprietary code base.

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