Archit Rathore

Machine Learning Engineer

New York City, New York, United States4 yrs 8 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Expert in Machine Learning and Topological Data Analysis.
Developed scalable NLP solutions for educational platforms.
Created innovative models for fraud detection in finance.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in Fintech and EdTech.

Contact

architrathore1@gmail.com LinkedIn

Skills

Core Skills

Machine LearningSoftware Development

Other Skills

APIsAlgorithmsApache SparkCC++Chatbot ServiceData AnalysisData PipelineData StructuresDistributed ProcessingETL PipelinesFraud DetectionHadoopHiveJava

About

I work on understanding ML models through the lens of Computational Topology and Visualization. I believe that the strong mathematical foundations of topological analysis and interactive capabilities through visualization techniques can help traverse the inherently complex structures of ML models, especially in the context of deep learning. I create frameworks for Topological Data Analysis (TDA) and visualization to analyze and reason about deep learning models, as a step towards explainable and interpretable ML.

Experience

Stripe

Machine Learning Engineer

Aug 2022 – Present · 3 yrs 7 mos · New York, New York, United States

Python (Programming Language)Machine Learning

Opensesame

Machine Learning Engineer Intern

May 2021 – Aug 2021 · 3 mos · Portland, Oregon, United States

· Built a data pipeline and NLP based chatbot service to provide course recommendation to learning admins.
· Created the architecture and APIs to deploy trained models in a scalable and secure manner.
· Planned and coordinated integration of the chatbot service with the recommendation system and frontend UI.
· Chatbot prototype identified as one of the key goals for the data science arm of the organization.

NLPData PipelineAPIsChatbot ServiceMachine Learning

Visa

Research Intern

May 2019 – Aug 2019 · 3 mos · San Francisco Bay Area

· Proposed a method to create auto-encoding models using Recurrent Neural Networks for financial transactions.
· Developed models to create embeddings for entities under weak labels.
· Implemented an end-to-end pipeline in PyTorch to interface with fraud detection and recommendation systems.
· Implemented efficient models capable of handling more than a million data points using pruning and distillation

Recurrent Neural NetworksPyTorchFraud DetectionRecommendation SystemsMachine Learning

Samsung r&d bangalore

Software Engineer

Jun 2016 – Jul 2017 · 1 yr 1 mo · Bangalore

· Built and optimized ETL pipelines for processing device logs for Samsung smartphones using Apache Spark.
· Proposed and implemented methods to find recurrent temporal patterns in smartphone usage and app activities.
· Deployed distributed processing pipelines for 500 million daily users with nearly 5 billion data points.
· Integrated the output from above into the intelligence module for Bixby in low-resource high-throughput setting.

ETL PipelinesApache SparkDistributed ProcessingSoftware Development