Dhruv AgnihotriLead Software Engineer @ Salesforce
Big Data|

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ABOUT ME

Computers are able to see, hear and learn. Welcome to the future.

Hi there! I am  Dhruv
A passionate programmer who loves all things code. I am a Full Stack Machine Learning Developer with experience in designing, developing and deploying end to end web apps which are used by many developers to automate their processes.

My interests include software design and development, artificial intelligence, machine learning, computer vision, and natural language processing.

Along with that, I also volunteer as a computer programming coach and teach computer science to students so that they are well exposed to this domain and can choose their career paths wisely.

I try to keep on top of all things tech and always have an eye for simplifying things to save time and energy that people put in doing manual tasks.

I graduated from University of Michigan, Ann Arbor with Masters in machine learning and data science.

Learn more about my:





Personal Projects


Experience


Salesforce

Salesforce

2019 - Present
LMTS & All Star Ranger

Key Responsibilities & Achievements:

  • Team Leadership: Led a team of six engineers in delivering complex integration projects, fostering a culture of innovation and excellence.
  • Package manager webApp: Engineered a dockerized web application that automated creating and push upgrading MCC package, previously a manual process.
  • Cloud Integration: Implemented JWT, web server flow, OAuth 2.0, and Connected App integration to set up bidirectional integration between Sales Cloud and Marketing Cloud.
  • Public Cloud Migration: Played a pivotal role in migrating applications to Salesforce's Hyperforce (public cloud), contributing to a scalable and efficient architecture.
University of Michigan

University of Michigan

2017 - 2018
Graduate Student Instructor

Key Responsibilities & Achievements:

  • Course Instruction: Delivered lectures and facilitated discussions for the CS403 course, covering advanced topics in machine learning and AI.
  • Assignment Development: Designed and graded assignments and exams, ensuring they met the highest academic standards.
  • Student Support: Provided one-on-one mentoring and group tutoring sessions, enhancing student understanding.
Denso Automotive

Denso Automotive

2017 - 2018
Intern

Key Responsibilities & Achievements:

  • Emotion Detection Project: Developed a cutting-edge emotion recognition system using deep learning techniques. Achieved a 69% accuracy rate in classifying emotions from video data, contributing to advancements in human-machine interaction.
  • Parameter Tuning: Utilized FastAI for model parameter optimization, enhancing the performance and reliability of the emotion detection system.
  • Collaborative Research: Worked closely with a multidisciplinary team of researchers and engineers, driving the project to successful completion and earning recognition for innovative contributions.
Shoptelligence

Shoptelligence

Summer 2018
Data Science Intern

Key Responsibilities & Achievements:

  • Data Warehouse Development: Developed a robust data warehouse and ETL pipeline to process clickstream data, resulting in a 30% increase in user engagement.
  • Data Ingestion & Transformation: Ingested, queued, and transformed data using Apache PULSAR and CockroachDB, significantly improving data processing efficiency.
  • Quality Assurance: Implemented over 800 unit tests using Pytest, ensuring high-quality code and reducing defects by 50%.
  • Machine Learning API: Developed an end-to-end machine learning API for image recognition and catalog item classification, enhancing product recommendations and user experience.
Altech Infrastructures

Altech Infrastructures

June 2015 - Aug 2017
Lead Data Analyst

Key Responsibilities & Achievements:

  • Real-Time Analytics: Developed an ETL pipeline proof-of-concept for real-time sensor data analysis using Spark streaming and Kafka. Improved data processing speed by 40% and enhanced system reliability.
  • Predictive Maintenance: Implemented predictive models to forecast the remaining useful life of boiler components, achieving a 10% improvement in accuracy and reducing downtime by 15%.
  • Optimization Modeling: Enhanced electricity load forecasting using SVM, reducing prediction errors by 3% and optimizing power distribution.
  • Data Analytics & Automation: Utilized R for emission ratio analysis, decreasing emission losses by 3%. Developed macros for data cleaning and automation, reducing analysis time by 30%.

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