- Software Engineer2017 - 2017Intuit
Technologies: Python, Java, Spring, Cassandra, Unix, Karate
- Worked as a full-time back-end engineer.
- Revamped the old data base systems into new ones.
- Contributed to changing how the release engineering cycle worked.
- Advanced Technology Consultant2016 - 2017Parent Powered
Technologies: Python, Dialogflow, Natural Language Toolkit, Twilio, Flask, Google API
- Used Flask and Python to create a virtual chatroom for multiple SMS users who don't have access to each other's phone number to promote anonymous collaboration and communication.
- Used Dialogflow to create and test a responsive chatbot that would respond to users via SMS with intelligent responses that were trained with previous conversations.
- Case-tested the chatbot by writing Python scripts that would hit Dialogflow's endpoint and simulate a user conversation.
- Supported the chatbot SMS interactions by using Twilio's integration service with Dialogflow.
- Software Engineer (Innovation and Advanced Technology Team)2016 - 2016Intuit
Technologies: Python, TensorFlow, PySpark, Apache Spark, Hive, HQL, SQL, Servers
- Worked closely with acclaimed individuals on topics such as deep learning, machine learning, and big data.
- Focused on how to use customer tax data with deep learning to improve user experience.
- Dove head first into applications involving TensorFlow, PySpark, and Hadoop.
- Participated in a company hackathon and was ranked one of the top 5 teams to compete.
- Learned how to innovate in a lean way, design for delight, and present technical knowledge in multiple lab meetings to over 30 participants.
- Research Assistant2015 - 2016Ryerson University
Technologies: Twitter API, Apache Spark, Scala, Java, SQL
- Worked closely with Dr. Cherie Ding and a PhD student to create a Twitter-based recommender system. I specifically focused on the gathering, storing, and the application of Twitter data.
- Created a program that pulled 3.5 million tweets from new users every day, with zero upkeep (using Scala). The data was stored in a database for later use, and it would automatically be updated as tweets were pulled in.
- Automatized the system and error-tested it to make sure the data was unique and supported.