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Priya Ganesan

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Priya Ganesan

About Me

Data-centric AI Engineer specializing in scalable infrastructure for machine learning. Leverages 2 years of experience optimizing real-time ETL pipelines at Goldman Sachs to design scalable AI architectures and automate end-to-end learning lifecycles in production.

Experience

Goldman Sachs

Software Engineer

  • Engineered high-performance real-time ETL pipelines using Kafka and Elasticsearch, processing over 12 Terabytes of daily financial data for optimized HDFS loading.
  • Optimized Parquet storage architecture via manual compaction strategies, achieving a 23% improvement in storage efficiency and significantly reducing downstream query latency for analytics teams.
  • Developed a PySpark-based automated validation framework with 32 custom constraints and PyTest integration, catching an average of 50+ critical data anomalies weekly prior to production ingestion.
  • Managed the integrity of a complex distributed ecosystem spanning 230 data groups and 18K+ datasets, reducing data availability incidents by 30% through proactive monitoring.
  • Implemented CI/CD pipelines via GitHub Actions to automate build, test, and deployment stages, accelerating release cycles from weekly to daily and eliminating manual deployment errors.
  • Onboarded 12+ new external datasets through custom web scraping and file-based ingestion workflows, transforming raw inputs into structured HDFS assets for dynamic reporting.

Goldman Sachs

Software Intern

  • Developed an interactive ReactJS + Redux dashboard to streamline the creation and management of securities within the firm’s internal database, improving data entry efficiency for analysts by ~40%.
  • Designed and implemented secure RESTful APIs for real-time communication between UI and backend systems, supporting thousands of daily internal user requests.
  • Optimized front-end components for responsiveness and performance, reducing page load times by over 30% to ensure a smoother workflow for financial analysts.
  • Collaborated with senior engineers to integrate the application into the production environment, gaining hands-on experience with agile practices in large-scale financial systems.

Education

Khoury College Of Computer Sciences, Northeastern University

Sept 2024 - May 2026

Master of Science in Artificial Intelligence

  • CGPA: 3.6/4.0
  • Current Coursework - Machine Learning Operations, Algorithms, Data Mining Techniques

Sri Sivasubramaniya Nadar College Of Engineering, Anna University

Aug 2018 - May 2022

Bachelor of Engineering in Computer Science

  • CGPA: 8.42/10

Projects

AudioSEEK: Timestamp-Grounded Spoiler Free Q&A for Audiobooks

Python, GCP (GKE), Terraform, RAG

Architected a multimodal RAG system transforming audiobooks into interactive Q&A; engineered a timestamp-alignment algorithm using FasterWhisper to ground LLM responses in specific audio segments. Implemented a "Spoiler-Safe" reasoning engine using Google Gemini and BGE-m3 embeddings.

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LLMPedia: AI-Powered Knowledge Platform

Python, LangChain, OpenAI API, Flask, ReactJS

Developed a context-aware knowledge platform leveraging large language models for information retrieval and conversational learning. Implemented RAG-based querying with structured prompt templates and a full-stack web interface.

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Team-Play Archetypes in League of Legends

Python, K-Means, PCA, Matplotlib, Scikit-learn

Built K-Means clustering models on a dataset of ~68K ranked players to classify gameplay behavior using metrics such as fighting efficiency, farming, vision control, and objective participation.

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Twitter Sentiment Analysis

ML, LSTM, BERT, LDA, Flask

Created a real-time sentiment classification system comparing ML models (Logistic Regression, SVM, Random Forest) and deep learning models (LSTM, BERT) using TF-IDF and Word2Vec. Deployed a Flask web app integrated with LDA topic modeling.

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Skills

Contact me

ADDRESS

23 Marcella, Roxbury
Boston, MA, USA – 02119

CONTACT NUMBER

+1 (857) 339-8452