Aashka Bhadresh Kapadia, Developer in Dubai, United Arab Emirates
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Aashka Bhadresh Kapadia

Verified Expert  in Engineering

Software Developer

Dubai, United Arab Emirates
Toptal Member Since
January 10, 2022

Aashka is a back-end developer specializing in Python, React, and Node.js. She excels in building customer-facing APIs, robust systems, and working with large volumes of data. At Flipkart, she managed a processing scale of one million orders per day, conducted inventory management for 700+ million listings, and re-engineered an inventory reservation forecast service, reducing run time from four hours to five minutes. Aashka enjoys solving problems for both enterprise clients and startups.


Flipkart Private Limited
Python, Java, Apache Spark, Hibernate, Apache Hive, Dropwizard, Stateless4j...
Goldman Sachs
Python, Java, Node.js, GraphQL, TypeScript, JavaScript, React, SQL, PostgreSQL...




Preferred Environment

Visual Studio Code (VS Code), IntelliJ IDEA

The most amazing...

...project I've re-engineered was an inventory reservation forecast service at Flipkart in Apache Spark, reducing the run time from four hours to five minutes.

Work Experience

Software Development Engineer

2020 - 2022
Flipkart Private Limited
  • Handled and managed a processing scale of one million orders per day and inventory management for 700+ million listings.
  • Reengineered and modeled an inventory reservation forecast service, which reduced the run time from four hours to five minutes. Headed walkthroughs and client negotiations to define contracts and milestones.
  • Engineered and deployed a new reporting microservice to fetch and merge data from MongoDB in Python using FastAPI.
  • Designed and implemented state machine and automatic triggers for complex entities using the Stateless4j library.
Technologies: Python, Java, Apache Spark, Hibernate, Apache Hive, Dropwizard, Stateless4j, FastAPI, Django, Django REST Framework, REST APIs, AWS Lambda, Amazon DynamoDB, Amazon S3 (AWS S3), Amazon EC2, HTML5, HTML, Bootstrap, jQuery, MongoDB, SQL, PostgreSQL, Back-end, Full-stack, SQLAlchemy, Apache, Object-oriented Programming (OOP), Async/Await, NoSQL

Technology Analyst, Controllers Division

2019 - 2020
Goldman Sachs
  • Modeled a new data architecture pipeline by liaising with global teams across the Americas, APEC, China, and EMEA. Developed E2E report generation in four weeks, which enabled the FRS to understand the evolving state of the economy during the pandemic.
  • Architected and built refiners by collaborating with cross-functional teams for the Apple credit card issued by GS, which enabled the filling of millions of credit cards per month.
  • Scoped and programmed flow for involuntarily terminated loans. Systematically automated the report generation, which eliminated human manual errors.
  • Introduced and developed a calendar BI solution in the reporting service dashboard, which increased visibility and reduced decision-making process time.
  • Instituted self-regulating data validation contracts and input standardization, which accelerated key business processes and brought down failure job run-time.
Technologies: Python, Java, Node.js, GraphQL, TypeScript, JavaScript, React, SQL, PostgreSQL, Flask, RDBMS, Object-oriented Programming (OOP)

Calorie Tracker APP

Developed a full-fledged, highly scalable, back-end microservice using Python and Fast API, with PGSQL as the database. The application has every common feature such as authentication, role management, authorization, and API projections. I utilized Nutritionix API to get food details and parsed those from the back end.

Image Style Transfer Using Deep Learning Neural Networks

Used deep learning neural networks for image style transfer. Recomposed an image in the style of another image using a convolutional neural network with transfer learning and generative adversarial networks in PyTorch.

Object Segmentation and Per-pixel Classification

Object segmentation and per-pixel classification of images using deep neural networks. Implemented deep learning algorithms such as convolutional neural network (CNN) and generative adversarial network (GAN) with residual connections in PyTorch.

Pandora’s JukeBox

Pandora’s Jukebox is a personalized recommendation engine based on a multi-armed bandit with content-based filtering. The recommendation is mainly based on the genre and artist. It pulls out songs based on your history, lets users explore music, and takes into account the cold start problem and the current mood of the user.

Image Forgery Detection

Image forgery detection and localization using copy-move regions approach. This contained blocked-based mapping that identifies patterns in images using principal component analysis (PCA) and expresses the data so that the internal structure of the data best explains the variance in the data. I also used keypoint-based mapping, which uses the SURF method (Speeded Up Robust Features) approach for local, similarity invariant representation and comparison of images.

Professional Provider System

A professional provider system that enables users to find and book professional service providers for different tasks according to the job description. Using software engineering paradigms such as requirement gathering, use-case diagrams, data flow diagrams, sequential models, and class diagrams. The project also handled the database normalization to satisfy the conditions stated for 1NF, 2NF, 3NF, and BCNF.
2015 - 2019

Bachelor's Degree in Computer Science

Dhirubhai Ambani Institute of Information and Communication Technology (DAIICT) - Gandhinagar, India


REST API (Intermediate) Certificate



Apache Spark 2.0



Node.js, REST APIs, React, SQLAlchemy, PyTorch, NumPy, TensorFlow, jQuery


IntelliJ IDEA, Apache, MATLAB


Django, Django REST Framework, Apache Spark, Hibernate, Dropwizard, Flask, Hadoop, Bootstrap


Python, SQL, Java, GraphQL, JavaScript, Java 8, TypeScript, HTML5, HTML, Python 3


Object-oriented Programming (OOP), REST


RDBMS, Apache Hive, PostgreSQL, NoSQL, MySQL, Databases, Amazon DynamoDB, Amazon S3 (AWS S3), MongoDB


Visual Studio Code (VS Code), Amazon Web Services (AWS), AWS Lambda, Amazon EC2


Computer Science, Async/Await, Natural Language Processing (NLP), FastAPI, Generative Pre-trained Transformers (GPT), Convolutional Neural Networks (CNN), GAN, Deep Learning, Normalization, APIs, Stateless4j, Back-end, Full-stack, Nutritionix API

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