Certified Scrum Product Owner (CSPO)

Data Analysis with R Programming

Google Business Intelligence Certificate

Machine Learning

Ask Questions to Make Data-Driven Decisions

Foundations: Data, Data, Everywhere

Neural Networks and Deep Learning

Decisions, Decisions: Dashboards and Reports

IBM Data Science Specialization

Advanced Learning Algorithms

 Supervised Machine Learning: Regression and Classification

Google Business Intelligence Specialization

Prepare Data for Exploration

Analyze Data to Answer Questions

Google Data Analytics Capstone: Complete a Case Study

Digital Transformation with Google Cloud

Machine Learning Specialization

ChatGPT for Beginners: Using AI For Market Research

AWS Cloud Practitioner Essentials

Process Data from Dirty to Clean

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Deep Learning Specialization

Structuring Machine Learning Projects

Google Data Analytics Certificate

Foundations of Business Intelligence

Unsupervised Learning, Recommenders, Reinforcement Learning

AI For Everyone

Google Data Analytics Specialization

Share Data Through the Art of Visualization

Pediatric Cancer Ward Volunteer We began visiting patients admitted to the Cancer Institute pediatric ward, as well as the outpatients who frequent the hospital for medication and checkups. Organizing and conducting programs and performances for special events, such as holidays, birthdays, Christmas, etc. We also attend events organized by the hospital, including National Cancer Consciousness week activities, "Relay for Life" with other cancer institutes/hospitals in the country, etc.

Outreach Club Vice President

Bachelor of Arts - BA

•   𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗡𝗟𝗣 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 processing 10,000+ daily voice calls, implementing 𝘂𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗠𝗟 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 (𝗧𝗼𝗽𝗶𝗰𝗥𝗮𝗻𝗸, 𝗧𝗲𝘅𝘁𝗥𝗮𝗻𝗸, 𝗬𝗔𝗞𝗘, 𝗔𝘂𝘁𝗼𝗣𝗵𝗿𝗮𝘀𝗲) for 𝗸𝗲𝘆𝗽𝗵𝗿𝗮𝘀𝗲 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻with 95% accuracy improvement.
  • 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹s on 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺, utilizing 𝗔𝘂𝘁𝗼𝗠𝗟 and 𝗕𝗘𝗥𝗧 for call segmentation and sentiment analysis, achieving 40% reduction in processing time. 
  • 𝗕𝘂𝗶𝗹𝘁 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗠𝗟 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 including automated model training, evaluation, and retraining pipelines using 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄, 𝗞𝗲𝗿𝗮𝘀, and 𝘀𝗰𝗶𝗸𝗶𝘁-𝗹𝗲𝗮𝗿𝗻, supporting 𝗚𝗣𝗨-𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀.
  •   • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝘀𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘀𝘆𝘀𝘁𝗲𝗺 combining 𝗚𝗼𝗼𝗴𝗹𝗲 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗔𝗣𝗜, 𝗩𝗔𝗗𝗘𝗥, and 𝗰𝘂𝘀𝘁𝗼𝗺 𝗕𝗘𝗥𝗧 𝗺

Natural Language Processing Engineer

Student Instructor @UC Berkeley

Software Architect | CTO

Math, Physics and Chemistry Tutor

Software Engineer

Software Design Engineer

𝗟𝗲𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘁𝘄𝗼 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗗𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁 𝗼𝗳 𝗗𝗲𝗳𝗲𝗻𝘀𝗲 𝗔𝗜 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀 (𝗣𝗢𝗟𝗔𝗥 & 𝗖𝗢𝗠𝗘𝗧), 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗲𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗺𝗶𝗹𝗶𝘁𝗮𝗿𝘆 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟭𝟬,𝟬𝟬𝟬+ 𝗮𝗰𝘁𝗶𝘃𝗲 𝘂𝘀𝗲𝗿𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀.

𝗣𝗢𝗟𝗔𝗥 - 𝗠𝗼𝗯𝗶𝗹𝗲 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗦𝘆𝘀𝘁𝗲𝗺
𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗺𝗼𝗯𝗶𝗹𝗲 𝗢𝗖𝗥 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 processing 3+ languages (Arabic, Chinese, French) with 95%+ accuracy for tactical document capture in 𝗻𝗲𝘁𝘄𝗼𝗿𝗸-𝗱𝗲𝗻𝗶𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀.
𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗠𝗟 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗟 𝗞𝗶𝘁 and 𝗧𝗲𝘀𝘀𝗲𝗿𝗮𝗰𝘁𝟰𝗔𝗻𝗱𝗿𝗼𝗶𝗱, reducing 𝗳𝗼𝗿𝗲𝗶𝗴𝗻 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 time from

Senior Software Engineer Technical Lead ( AI / ML )

Teacher's Assistant for General Chemistry Courses

• 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 using 𝗥𝗔𝗦𝗔 and 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗶𝗮𝗹𝗼𝗴𝗳𝗹𝗼𝘄 𝗖𝗫, achieving 𝟵𝟴% 𝗶𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 and 𝟵𝟱% 𝗱𝗶𝗮𝗹𝗼𝗴𝘂𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 for Digital Person educational assistant proof-of-concept at Maryville University.
  • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗰𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗼𝗻 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 utilizing 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲s, 𝗖𝗹𝗼𝘂𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, 𝗔𝗽𝗽 𝗘𝗻𝗴𝗶𝗻𝗲, and 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟 to support 24/7 availability for educational chatbot system serving university students.
  • Built 𝗥𝗲𝗮𝗰𝘁.𝗷𝘀 frontend and 𝗡𝗼𝗱𝗲.𝗷𝘀/𝗘𝘅𝗽𝗿𝗲𝘀𝘀.𝗷𝘀 backend with 𝗥𝗘𝗦𝗧𝗳𝘂𝗹 𝗔𝗣𝗜𝘀 to orchestrate multiple 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 and third-party integrations, improving system response times and user experience.
  • 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗰𝘂𝘀𝘁𝗼𝗺 𝗡𝗟𝗣 𝗽𝗶𝗽𝗲

• 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗮𝗻𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗪𝗦 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 supporting AI-powered 𝘀𝗽𝗲𝗲𝗰𝗵-𝘁𝗼-𝘀𝗽𝗲𝗲𝗰𝗵 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 for professional 𝘃𝗶𝗱𝗲𝗼 𝗱𝘂𝗯𝗯𝗶𝗻𝗴. 
• 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗹𝗮𝗿𝗴𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹 𝗔𝗣𝗜𝘀 𝘄𝗶𝘁𝗵 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 
• Built r𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 and observability systems using 𝗦𝗲𝗻𝘁𝗿𝘆 and 𝗟𝗼𝗴𝗴𝗹𝘆 for 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 and system health. 
• 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗰𝗼𝗿𝗲 𝗔𝗣𝗜 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 interfacing with 𝗹𝗮𝗿𝗴𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 for 𝘀𝗽𝗲𝗲𝗰𝗵 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻, 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻, and 𝘁𝗲𝘅𝘁-𝘁𝗼-𝘀𝗽𝗲𝗲𝗰𝗵 𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀.
• 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗺𝘂𝗹𝘁𝗶-𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 and 𝗺𝘂𝗹𝘁𝗶-𝗮𝗰𝗰𝗲𝗻𝘁 𝗱𝘂𝗯𝗯𝗶𝗻𝗴 

Senior Software Architect

•  𝗪𝗼𝗿𝗸𝗲𝗱 𝗼𝗻 𝗰𝗿𝗼𝘀𝘀-𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 converting 𝘀𝗽𝗲𝗲𝗰𝗵-𝘁𝗼-𝘁𝗲𝘅𝘁 using 𝗚𝗼𝗼𝗴𝗹𝗲 𝗦𝗽𝗲𝗲𝗰𝗵 𝗔𝗣𝗜, reducing manual transcription costs by 80% while maintaining 98% transcription accuracy.
 • 𝗕𝘂𝗶𝗹𝘁 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 in 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 connected to 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, enabling data-driven insights through complex SQL transformations and automated data pipelines
 • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 for ML model serving with 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻, handling 50,000+ daily requests with 99.9% uptime and sub-200ms response times.
  • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗘𝗧𝗟 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 ingesting terabytes of data from 𝗖𝗹𝗼𝘂𝗱𝗦𝗤𝗟 to 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 with continuous daily processing, supporting 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 and 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀.

Data Engineer | Data Scientist

Software Product Management Intern

Undergraduate Student Researcher

CS61A Lab Assistant

• 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 processing student interaction events using 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝗼𝘀𝗲r, and 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, reducing data latency from hours to minutes and enabling near 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗲𝗻𝗿𝗼𝗹𝗹𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀.
  • 𝗕𝘂𝗶𝗹𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗘𝗧𝗟 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 integrating 8+ data sources (𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲, 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿, 𝗟𝗠𝗦 𝗖𝗮𝗻𝘃𝗮𝘀, 𝗙𝗶𝘃𝗲𝟵) using 𝗙𝗶𝘃𝗲𝘁𝗿𝗮𝗻 and 𝗗𝗕𝗧, achieving high availability and supporting daily analytical workloads.
  • 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗠𝗟-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗰𝗮𝗹𝗹 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 leveraging 𝗚𝗖𝗣 𝗦𝗽𝗲𝗲𝗰𝗵-𝘁𝗼-𝗧𝗲𝘅𝘁 and 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗔𝗣𝗜𝘀 to process customer calls and extract 𝘀𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 for call center 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻.
  • 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 

Google Data Engineer | Natural Language Processing Engineer

AI, Data Pipelines, Civic Infrastructure. 
• Design and build AI-powered document ingestion and processing pipelines.
• Integrate OCR and LLM APIs to extract structured data from scanned legal documents.
• Develop and improve tools for prompt engineering, model output validation, and data
transformation.
• Collaborate on backend systems that support processing, tracking, and secure storage of
document data.
• Support data scientists and ML engineers with infrastructure and tooling to evaluate
models and refine prompts.
• Write production-grade, maintainable code to support both experimentation and long-term
reliability.

Senior AI/ML Software Engineer

• 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗵𝗶𝗴𝗵-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 for 𝗥𝗔𝗚 applications, handling PDFs, images, HTML, and Word documents using 𝗣𝘆𝗧𝗼𝗿𝗰𝗵, 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 (𝗬𝗢𝗟𝗢𝗫, 𝗬𝗢𝗟𝗢-𝗡𝗔𝗦, 𝗗𝗲𝘁𝗲𝗰𝘁𝗿𝗼𝗻𝟮), and 𝗚𝗣𝗨-𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗢𝗖𝗥, scaling to 100K+ documents daily with enterprise-grade reliability.
 • 𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗼𝗿 with 300+ merged PRs and 500+ code reviews on GitHub, building developer tools used by 50K+ engineers globally.
  • 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 resolving memory bottlenecks and implementing distributed processing, improving throughput 40% while reducing infrastructure costs.
  • 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱 𝟭𝟬+ 𝗟𝗟𝗠 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀 (𝗢𝗽𝗲𝗻𝗔𝗜, 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰, 𝗚𝗼𝗼𝗴𝗹𝗲) implementing 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, directly impacting $2M+ customer pipeline.
  • 𝗔�

Senior Machine Learning Engineer

• 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝗱 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗲𝗻𝗴𝗶𝗻𝗲 using 𝗕𝗘𝗥𝗧/𝗦𝗕𝗘𝗥𝗧 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 for 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, achieving 92% precision in 𝗠𝗜𝗧𝗥𝗘 𝗔𝗧𝗧&𝗖𝗞 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 with novel embedding optimization techniques.
  • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗣𝗢𝗖 𝗠𝗟𝗢𝗽𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 with 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄/𝗣𝘆𝗧𝗼𝗿𝗰𝗵 for 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘁𝗵𝗿𝗲𝗮𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, demonstrating 94% reduction in manual security gap identification time through 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗮𝘁𝘁𝗮𝗰𝗸-𝗱𝗲𝗳𝗲𝗻𝘀𝗲 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻. 
  • 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗖𝗨𝗗𝗔-𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 processing 10K+ document pairs with 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗯𝗮𝘁𝗰𝗵 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲, validating scalability for enterprise cybersecurity analytics applications.
  • 𝗕𝘂𝗶𝗹𝘁 𝗥𝗲𝗮𝗰𝘁 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗳𝗼𝗿

Machine Learning Engineer

• 𝗕𝘂𝗶𝗹𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗠𝗟𝗢𝗽𝘀 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 using 𝗠𝗲𝘁𝗮𝗳𝗹𝗼𝘄 workflows and 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀  for 𝗬𝗢𝗟𝗢-𝗯𝗮𝘀𝗲𝗱 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝗼𝗻 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴. 
  • Refactored standalone scripts into structured 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 with FrameServerHandler. Reduced processing latency by 75% with real-time performance optimization.
  • Developed "Thoth v0" 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 with bidirectional 𝗙𝗶𝗳𝘁𝘆𝗢𝗻𝗲-𝗘𝗻𝗰𝗼𝗿𝗱 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 for 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀. Created 5-step automated 𝗮𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 from data sampling to model retraining. Accelerated Machine Learning cycles by 65% using 𝗔𝗪𝗦 𝗦𝟯 and 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 backend.
  • 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗳𝗮𝗰𝘁𝗼𝗿𝘆 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 with 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗯𝗮𝘁𝗰𝗵𝗶𝗻𝗴 for sequential video processing. Implemented sequence_id-based or

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗮𝗻𝗱 𝗹𝗲𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰 𝗛𝗲𝗮𝗹𝘁𝗵 𝗥𝗲𝗰𝗼𝗿𝗱 (𝗘𝗛𝗥) 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘀𝗲𝗿𝘃𝗶𝗻𝗴 𝗧𝗲𝘅𝗮𝘀-𝗯𝗮𝘀𝗲𝗱 𝗵𝗼𝘀𝗽𝗶𝘁𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺. 

  • 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗰𝗹𝗼𝘂𝗱-𝗯𝗮𝘀𝗲𝗱 𝗘𝗛𝗥 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 on 𝗔𝗪𝗦 (𝗘𝗖𝟮, 𝗥𝗗𝗦, 𝗩𝗣𝗖, 𝗘𝗖𝗦, 𝗘𝗖𝗥) supporting 10,000+ patient records with 99.9% uptime.
  • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗻𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 including patient registration, hospital systems, and external service integrations.
  • 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻t with 𝗔𝗪𝗦 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗖𝗹𝗼𝘂𝗱, multi-tier security, and automated scaling capabilities.
  • 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗦𝗼𝗻𝗶𝗰𝘄𝗮𝗹𝗹 𝗳𝗶𝗿𝗲𝘄𝗮𝗹𝗹 and established secure network architecture with comprehensive monitoring.
  • 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗛𝗜𝗣𝗔𝗔 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼

Senior Software Engineer Technical Lead

ML Engineer

github

Know about Christine Straub's work experience, education, bootcamps, certifications, and much more. Christine Straub