About me

I’m a seasoned software developer with a passion for system design and expertise in distributed applications. My focus is in leveraging AI to solve complex challenges, with a strong emphasis on big data and scalable platform development. My experience includes optimizing backend API flows, automating processes for efficiency, and integrating advanced algorithms in diverse settings such as Swiggy and Siemens. I’m open to new challenges and eager to explore innovative technological solutions. Feel free to connect with me on LinkedIn or via email for further discussions.

What I do

  • Web development icon

    Software development

    I develop software applications for a better tomorrow.

  • design icon

    Distributed systems

    I’m interested in designing and implementing robust and scalable solutions that address various challenges in distributed systems.

  • camera icon

    Photography

    I like taking photographs of historical and monumental places.

Testimonials

  • Abhishek Bose

    Abhishek Bose

    abhishekbose550

    Chaitanya is one of the finest engineers I’ve worked with. Besides the fact that he is a very competent machine learning engineer, his ability to calmly endure any problem statement and take it to the finish line really shines. We worked together in Swiggy and collaborated on setting up the ML inference pipeline for PyTorch models. His contributions in both the LLD and execution has been incredible. I look forward to working with him in the future, and I’m sure that whichever organization he joins would greatly benefit from his talents.

  • Yaswanth Naga Sai Nalluri

    Yaswanth Nalluri

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    I’ve had the pleasure of working with Chaitanya for almost 1 and a half years at Stylumia, where he has been a valuable member of our computer vision team. Chaitanya is a highly skilled computer vision engineer with a strong track record of success. He has solved complex problems at an industry scale, including Image feature extraction and classification. He is also an excellent problem solver, and he is always able to find creative and innovative solutions to complex problems. In addition to his research skills, Chaitanya is also an excellent communicator. He is able to clearly explain complex technical concepts to both technical and non-technical audiences. He has presented quite a few research papers to the entire company and helped build use-cases on top of the research papers. I have no doubt that Chaitanya would be an asset to both research teams and application teams.

  • Behnam Torabi

    Behnam Torabi

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    I enjoyed working with Chaitanya on machine learning, machine vision, and Android development projects. His technical expertise was impressive, particularly in image recognition and mobile app development. Chaitanya consistently delivered innovative, high-quality solutions and was a quick learner, always eager to adopt new technologies. I highly recommend him for any related role.

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Resume

Education

  1. University of Texas at Dallas

    Aug 2023 — ongoing

    Master of Science — M.S in Computer Science

    Grade: 4.0/4.0

    Relevant Coursework:

    Database Design
    Advanced Operating Systems
    Intro to Multicore Programming
    Design and Analysis of Computer Algorithms
    Big Data Analysis
    Network Security

  2. Indian Institute of Information Technology, Sri City

    Aug 2016 — Jun 2020

    Bachelor of Technology — BTech (Honors) in Electronics and Communications Engineering

    Grade: 8.66/10

    Relevant Coursework:

    Data Structures and Algorithms
    Object Oriented Programming in Java
    Probability Theory
    Computer Vision
    Machine Learning

Experience

  1. Software Engineer @ Copart

    Aug 2024 — ongoing | Dallas, TX

    Technologies used:

    Java
    Spring Boot
    Spring Batch
    Spring Security
    Apache Kafka
    Apache Solr
    Redis
    MariaDB
    Angular

  2. Graduate Research Assistant

    Jan 2024 — Aug 2024 | Richardson, TX

    Developed a chatbot application for UTD-related queries using Retrieval-Augmented Generation (RAG), with UTD documents and web page content stored in S3 and their vector representations managed in OpenSearch for efficient query similarity search.

    Deployed a fine-tuned Llama 3.1 model on AWS Bedrock, enabling accurate and context-aware responses to user questions.

    Implemented a FastAPI backend with LangChain to process chatbot queries, scaling dynamically based on the traffic load using EKS.

    Configured Elastic Load Balancer (ELB) to ensure efficient traffic distribution, high availability, and optimal performance under varying request volumes.


    Technologies used:

    Python
    PyTorch
    Ultralytics
    Java
    ONNX runtime
    DJL
    Spring Boot

  3. Software Dev Engineer 2 @ Swiggy

    Oct 2022 — Jul 2023 | Bangalore, India

    Designed PyTorch model serving capability within the Spring Boot codebase using the Deep Java Library and Nvidia Triton for our in-house data science platform, enabling PyTorch integration for production serving and reduced TensorFlow model memory usage by 30%.

    Implemented robust platform cost monitoring mechanisms, leading to a significant reduction in the Data Science team's compute usage by approximately 45%

    Developed an observability tool using Databricks, Kafka, and Spark, hosting and monitoring 80+ deployed data science models in real-time, improving governance and reducing response times by 20%.


    Technologies used:

    Apache Spark
    Java
    Scala
    Apache Kafka
    Apache Flink
    Lightbend Akka
    Nvidia Triton

  4. Machine Learning Engineer 1 @ Swiggy

    Dec 2021 — Sept 2022 | Bangalore, India

    Expanded the reach of smart push notifications to 20 million users, while drastically reducing Spark job runtime from 9 hours to 2 hours and cutting compute costs by ~75% (from $688.74 to $166.84). This resulted in a daily order increment of 117.6 on average and a 14% decrease in push notification uninstall rates

    Improved the spell correction TensorFlow model inference time to under 100ms, successfully tested at a load of 1000 requests per second

    Developed a proof-of-concept (POC) for an insights dashboard aimed at providing actionable competitor insights to restaurant partners


    Technologies used:

    Apache Spark
    PyTorch
    Scala
    Databricks

  5. Data Science Engineer @ Stylumia

    Jun 2020 — Dec 2021 | Bangalore, India

    Led development using SQS, Elasticsearch, HuggingFace, and FastAPI, achieving 1200 RPS throughput and 30 ms P99, which increased user engagement by 15%.

    Automated the image-tagging process for customer-specific report generation, cutting lead time by 50% and resulting in an 18% increase in customer conversion rate

    Spearheaded the use of Jenkins for microservice deployment automation through CI/CD pipelines and established a culture of thorough code reviews, enhancing deployment speed and code quality.


    Technologies used:

    ElasticSearch
    FastAPI
    PyTorch
    MLFlow
    Optuna
    AWS SQS
    AWS S3

  6. Research Intern @ Siemens

    Jan 2020 — Jun 2020 | Bangalore, India

    Engineered an attention-based algorithm to estimate weight and water content from potato images, increasing estimation accuracy by 8% and reducing processing time by 30%.

    Integrated the algorithm into an internal food quality assessment tool being prototyped at Siemens, using Django and Docker for seamless operation


    Technologies used:

    Django REST Framework
    TensorFlow
    Docker

  7. Intern @ Flutura

    Jun 2019 — Jul 2019 | Bangalore, India

    Contributed to the development of the chatbot feature on Flutura's Cerebra platform. The project used the Django REST framework, PostgreSQL, and DialogFlow for the backend, and Angular for the frontend

    Responsible for adding new features and managing existing dialogue options in the backend part of the project


    Technologies used:

    Django REST Framework
    PostgreSQL
    Dialog-Flow

Publications

  1. DFW-PP: dynamic feature weighting-based popularity prediction for social media content

     Journal of supercomputing · Oct 5, 2023

  2. Truthformers at Factify 2022: Evidence aware Transformer based Model for Multimodal Fact Checking

    Factify shared task at AAAI · Mar 1, 2022

  3. Transformer Ensemble System for Detection of Offensive Content in Dravidian Languages

     FIRE 2021: Forum for Information Retrieval Evaluation · Dec 13, 2021

  4. Single Image Dehazing Using Improved CycleGAN

     Journal of Visual Communication and Image Representation · Dec 30, 2020

Skills

C/C++
Python
Java
Scala
Javascript
Object Oriented Programming
SQL
Spring Boot
Apache Spark
Apache Kafka
Redis
Elasticsearch
Machine Learning
Deep Learning
Computer Vision
Docker
PyTorch
TensorFlow
HTML
CSS
Git
Apache Hive

Projects

Blogs