Skills

Languages

Python, Javascript/Typescript, Go, Clojure, Java, C/C++, Bash, LaTeX

Libraries & Frameworks

FastAPI, Django, Node.js, Express.js, Ruby on Rails, Gin, React.js, Next.js, Angular, TensorFlow, PyTorch, Scikit-learn

Data, DevOps, & Infra

AWS/GCP, Docker, Kubernetes, Terraform, Spark, Hadoop, PostgreSQL, MySQL, PostgreSQL, MongoDB, BigTable, Cassandra, DynamoDB, Redis

Experience

 
 
 
 
 

Software Engineer (Full-Stack)

Fractl Inc.

Nov 2023 – Present Silicon Valley, USA (Remote)

Making instrumental contributions to the Fractl programming language and Design Studio — a no-code/low-code and DevOps platform with integrated AI co-pilot.

  • Using meta-programming, added functionality for GraphQL layer auto-generation for Fractl apps to enhance data retrieval efficiency, enabling more flexible and powerful querying capabilities. Relevant PRs: PR 1, PR 2.
  • Developed rule-based and ML hybrid technique for automated relational schema mapping to Fractl code.
  • Designed, developed, and deployed the backend for LLM copilot and integrated with Fractl Design Studio.
  • Added nREPL support for Fractl to enable cross-platform remote code execution along with support for interactive development using popular IDEs (Emacs, Vim, IntelliJ). Relevant PR.
  • Reduced time required to integrate third-party APIs by over 70% by developing Fractl code generator — based on OpenAPI Code Generator — to automatically generate schema, events, dataflows, and resolvers given OpenAPI spec.
  • Successfully reorchestrated containerization and deployment microservice, drastically reducing Fractl app deployment time by over 90%.
  • Enabled real-time synchronization between browser and GitHub code changes.
  • Using GitHub webhooks, engineered microservice to automate the redeployment of client applications in response to code changes.
  • Engineered module for unified and fine-grained control over server-side error logging and client-side error responses. Relevant PRs: PR 1, PR 2.
  • Technologies: Clojure, Java, JavaScript, SQL, Docker, Kubernetes, AWS.
 
 
 
 
 

Software Engineer (Backend)

Motive

Oct 2022 – Nov 2023 San Francisco, California (Remote)

Worked on the backend of the Motive fleet card product, fleet management portal, driver mobile app, and Motive internal admin portal.

  • Led several projects end-to-end by transforming product requirements into technical designs and developing solutions involving diverse databases, microservices, background jobs, message queues, and third-party APIs (e.g., Plaid, Marqeta, Alloy) with key contributions in real-time card management, payment & transaction processing, ledger management, and API gateways.
  • Played an instrumental role in the backend design and development of ATM withdrawal feature that unlocked ~$80 million TPV boost.
  • Enhanced user experience by reducing bank statement submission time by around 30% during the automated underwriting process.
  • Achieved an 1800% speed increase in critical job executions through optimization, SQL-level processing, and caching.
  • Worked on PostgreSQL database replica setup and offloading read queries to it for improved response time and fault tolerance, along with comprehensive guidelines for developers to minimize/deal with stale data.
  • Actively discovered and fixed tech debt, contributions to the email module resulted in ~200% increase in emails and ~35% reduction in overdue invoices (>$1 million).
  • Developed dynamic dashboards to monitor business and performance metrics, along with real-time anomaly detection and automated alerts.
  • Maintained over 95% unit test coverage through Test-Driven Development.
  • Technologies: Ruby on Rails, Python, PostgreSQL, DataDog, Redash, Kafka, Docker, Kubernetes, Terraform, AWS.
 
 
 
 
 

Software Engineer (Full-Stack)

Accrue Inc.

Jul 2021 – Jan 2021 New York, United States (Remote)

Developed, optimized, and deployed statistical predictive models, microservices, and API endpoints for generating pattern summaries in large amounts of historical stock and forex data.

  • Led a team of 3 in infrastructure and backend revamp, including automated CI/CD pipeline creation and GraphQL layer on PostgreSQL, resulting in a remarkable over 500% increase in productivity.
  • Headed a front-end overhaul, resulting in a cutting-edge, responsive, and visually striking UI. Engineered sophisticated data processing, advanced charting, and analysis components.
  • Technologies: Express.js, React.js, Clojure, PostgreSQL, Cassandra, GraphQL (Hasura), GCP (Bigtable), AWS (RDS, EC2, CodePipeline, CodeDeploy), Docker.
 
 
 
 
 

Software Engineer (Full-Stack)

AI Lounge

Mar 2021 – Jun 2021 Pakistan (Remote)
  • Single-handedly created a highly-functional MVP of a web-based no-code machine learning platform for kids to experiment with AI models.
  • Worked on custom Scratch blocks for sentimental analysis, voice recognition, text-to-speech conversion, and image classification.
  • Technologies: Express.js, Python, React.js, Handlebars.js, MySQL, Docker.
 
 
 
 
 

Software Engineering Intern

Water Sprint

Feb 2021 – Mar 2021 Islamabad, Pakistan (Remote)
  • Implemented API endpoints and contributed to the development of the authentication system for a water-management application.
  • Technologies: Express.js, Python, PostgreSQL, AWS, Swagger.

Projects

.js-id-include
All Software Development Machine Learning Databases Web

Narrowed down potentially anomalous connections (<2K) amongst 4.9 million records of network traffic through clustering using KNN …
Code

ACID-compliant relational database with a B-Tree-based key-value store, created from scratch.

End-to-end object detection model using Transformers. Images are passed to ResNet backbone to extract a feature vector of size 256 …
Code

An end-to-end ConvNet model, wrapped in a GUI, to predict and visualize steering angle for each input video frame. Technologies used: …
Code

Developed a software solution for storing land registry papers in an immutable manner, mitigating issues such as false land ownership …
Code

Facial landmarks are extracted from detected faces using OpenCV. Then, faces are normalized using affline transformation and fed to a …
Code

We achieved accuracy of 97%, recall of 0.96, precision of 0.96, and F1 score of 0.95. It was a group project, we implemented a system …
Project Report

An elegant and responsive web app with a secure authentication system that scales to millions of users and supports real-time …
Website

CycleGAN implementation from scratch using two generators and discriminators, used for style transfer between apples and oranges. …
Code

Detecting violence such as fighting scenes in videos using two-stream CNNs and LSTMs. Technologies used: Python, TensorFlow, OpenCV, …
Code

Recent Posts

Ever witnessed a feature rollout gone terribly wrong, causing chaos for users? Here's the secret to high-speed feature rollout without …
Last updated on 2 min read

Recent & Upcoming Talks

Discussed UG plan of action to advance career as a researcher and provided detailed pathways for roles in tech industry.
May 28, 2021 1:00 PM Online

Accomplish­ments

Course: Cloud Systems Software

Georgia Institute of Technology Sep 2023 – Sep 2023
This course provides an introduction to programming frameworks and their implementation issues in the Cloud. It explains multiple topics, including: scalable distributed data stores, resource management (for supporting multi-tenancy and elasticity) and virtualization techniques. Optionally, the student will also be guided in the implementation of a basic version of the distributed runtime system for the Map-Reduce programming framework
See certificate

Course: Software Defined Networking

Georgia Institute of Technology Sep 2023 – Sep 2023
This course provides an introduction to data center networking technologies, more specifically software-defined networking. It covers the history behind SDN, description of networks in data-centers, a concrete data-center network architecture (Microsoft VL2), and traffic engineering.
See certificate

Course: Software Architecture Patterns for Big Data

University of Colorado Boulder Jul 2023 – Sep 2023
The course is intended for individuals looking to understand the architecture patterns necessary to take large software systems that make use of big data to production. You will transform big data prototypes into high quality tested production software. After measuring the performance characteristics of distributed systems, you will identify trouble areas and implement scalable solutions to improve performance. Upon completion of the course you will know how to scale production data stores to perform under load, designing load tests to ensure applications meet performance requirements.

Course: Designing Data-Intensive Applications

Universidad Nacional Autónoma de México Jul 2023 – Sep 2023
By the end of this specialization, learners will be able to propose, design, justify and develop high reliable information systems according to type of data and volume of information, response time, type of processing and queries in order to support scalability, maintainability, security and reliability considering the last information technologies.
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Course: 6.824: Distributed Systems

MIT Apr 2023 – Jul 2023
This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: server design, network programming, naming, storage systems, security, and fault tolerance. The assigned readings for the course are from current literature.

Competition: 1st Position in NEO Code Hunt

IEEE GIKI Mar 2021 – Mar 2021
An intervarsity competitive programming competition, teams from universities all across Pakistan participated.
See certificate

Competition: 2nd Position in CodinGuru 3.0

IEEE LUMS Jan 2021 – Jan 2021
One of the largest national-level competitive programming competition, teams from universities all across Pakistan participated.
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Competition: Winner in Machine Learning and Data Analytics category of ACM InNOVAtion Cup

NUST ACM Student Chapter Dec 2020 – Dec 2020
It was an intraversity competition, problems were prepared by the partner companies. I was offered internship at WaterSprint for the role of Machine Learning Engineer.
See certificate

Competition: 2nd Position in Computer Vision Competition for Classification of 20K Unique Sketches into 20 Categories

Pakistan Pattern Recognition Society Aug 2019 – Aug 2019
Participated while volunteering as an organizer at 3rd IAPR Summer School on Document Analysis along with two other peers.

Course: Convolutional Neural Networks in TensorFlow

DeepLearning.AI Jul 2019 – Jul 2019
See certificate

Course: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

DeepLearning.AI Jul 2019 – Jul 2019
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Course: Natural Language Processing in TensorFlow

DeepLearning.AI Jul 2019 – Jul 2019
See certificate

Course: Mathematics for Machine Learning: Linear Algebra

Imperial College London Jul 2019 – Aug 2019
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Course: Sequence Models

DeepLearning.AI Jul 2019 – Jul 2019
Learned to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs. Applied RNNs to Character-level Language Modeling and gained experience with natural language processing and Word Embeddings. And used HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.
See certificate

Course: Structuring Machine Learning Projects

DeepLearning.AI Jun 2019 – Jul 2019
Learned to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and applying end-to-end learning, transfer learning, and multi-task learning.
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Course: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

DeepLearning.AI Jun 2019 – Jul 2019
Learned the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; studied the use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implementing and applying a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and checking for their convergence; and implementing a neural network in TensorFlow.
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Course: Convolutional Neural Networks

DeepLearning.AI Jun 2019 – Jul 2019
Created convolutional neural networks, including recent variations such as residual networks; applied convolutional networks to visual detection and recognition tasks; and used neural style transfer to generate art and applied these algorithms to a variety of image, video, and other 2D or 3D datasets.
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Course: Neural Networks and Deep Learning

DeepLearning.AI Jan 2019 – Feb 2019
Became familiar with the significant technological trends driving the rise of deep learning; Learned to build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to various applications.
See certificate