Aashka is available for hire

Aashka Bhadresh Kapadia

Verified Expert in Engineering

Software Developer

Location

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.

Computer Science Python REST APIs SQL Node.js Django Django REST Framework RDBMS Natural Language Processing (NLP)Visual Studio Code (VS Code)Java Apache Spark Hibernate Apache Hive Dropwizard Async/Await Apache

Portfolio

Flipkart Private Limited

Python, Java, Apache Spark, Hibernate, Apache Hive, Dropwizard, Stateless4j...

Goldman Sachs

Python, Java, Node.js, GraphQL, TypeScript, JavaScript, React, SQL, PostgreSQL...

Experience

Python - 3 years Visual Studio Code (VS Code) - 3 years Django - 2 years Node.js - 2 years Natural Language Processing (NLP) - 1 year Amazon Web Services (AWS) - 1 year Generative Pre-trained Transformers (GPT) - 1 year GraphQL - 1 year

Availability

Part-time

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)

Experience

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

https://docs.google.com/presentation/d/1DXIv0zQnQAq6SfVqeIxfk9LMhjg5Q8dLciAr58ls5L0/edit

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

https://docs.google.com/presentation/d/17SgmYHb7Pr4Ct3gcBFEM0rFaY_mh_NPTgSAb4UsXDhQ/edit?usp=sharing

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

https://docs.google.com/presentation/d/1LoDeywhuSkPwwMBXBi2NfzvQxfXso4hzP8WLAtnyGDM/edit?usp=sharing

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

https://docs.google.com/presentation/d/1qjbbrKqGxAtaRmQsC-6n539CnL5OguX_BFOnx4baSks/edit?usp=sharing

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.

Skills

Languages

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

Frameworks

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

Libraries/APIs

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

Tools

IntelliJ IDEA, Apache, MATLAB

Paradigms

Object-oriented Programming (OOP), REST

Storage

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

Other

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

Platforms

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

Education

2015 - 2019

Bachelor's Degree in Computer Science

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

Certifications

APRIL 2021 - PRESENT

REST API (Intermediate) Certificate

HackerRank

MARCH 2021 - PRESENT

Apache Spark 2.0

Udemy

Collaboration That Works

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