A senior software engineer equipped with production-level knowledge about Rust, Go, Scala, TypeScript, Node.js, low-layer programming, and Clean Architecture. Enthusiastic about programming languages and their implementations.
A web version of this resume is at https://raviqqe.com/resume/.
Open Source Projects
- The functional programming language inspired by Go’s philosophy.
- Automatic thread-safe memory management by the Perceus reference counting algorithm.
- Foreign Function Interface (FFI) against Rust supporting both non-
async functions using proc macros and the C ABI.
Stak (Rust, Scheme)
- A Scheme virtual machine and ahead-of-time bytecode compiler based on Ribbit Scheme.
alloc and no
- Designed to run on a machine stack as well as on heap.
- Small memory footprints (~ a few kilobytes.)
- Performance comparable with Gambit Scheme.
- The blazingly fast Scheme code formatter.
- Built with the new allocator API (available only in nightly Rust as of August 2023) for arena memory allocation in parsing and formatting algorithms.
- A Ninja-compatible build system for high-level programming languages.
- Asynchronous task execution and scheduling integrated with Tokio runtime.
- Performance comparable with Ninja (0.4 time slower) despite of compact implementation.
- Compiler infrastructure and its intermediate language for functional programming languages inspired by C— in Glasgow Haskell Compiler.
- Supports compilation of the intermediate language into both LLVM IR and C.
- The rustic MLIR bindings for Rust.
- Automatic API generation by proc macros and TableGen (WIP.)
- The fast website link checker.
- Parallel check of URL liveness using goroutines and channels.
For other projects, see my portfolio page at raviqqe.com.
- Period: September 2018 - December 2022 (4 years), Canada
- Position: Algorithm Engineer, Full Stack Engineer, and Site Reliability Engineer (full time)
- Development of an on-demand transit system.
- Including RESTful API and rider-driver matching backend systems, an admin web application, and rider/driver mobile applications.
- Development of rider-driver matching systems.
- Development of a traffic information system and its integration with a routing system.
- Efficiency and quality optimization of rider-driver matching systems.
- Design and implementation of driver navigation API integrated with a routing system.
- Optimization of relational database queries on PostgreSQL.
- Design and implementation of mass transit integration and whole trip planning using General Transit Feed Specification (GTFS).
- Design and implementation of zero-downtime PostgreSQL data migration across different cloud vendors.
- Development of GitOps pipelines using GitHub, ArgoCD and Kubernetes.
- Design and implementation of a Prometheus/Grafana telemetry stack on a Kubernetes cluster.
- Pipeline and task automation for an infrastructure stack of Kubernetes, Helm, Terraform, and Google Cloud.
- Monolith-to-microservice migration of a RESTful API service.
- Scala, TypeScript, Node.js, React, React Native, HashiCorp Configuration Language, and shell scripts are used throughout projects listed above.
- Period: May 2017 - August 2017 (4 months), Malaysia
- Position: Full Stack Engineer (full time)
- MVP implementation of a personal assistant application that completes tasks, such as posting on social media and requesting Uber rides on behalf of users through voice interfaces.
- Development of Deep Learning models of language model and voice recognition, Python, TensorFlow, TypeScript, React Native, GraphQL, Firebase, and Google Cloud Platform.
Toyota Central R&D Labs.
- Period: March 2015 - April 2015 (2 months), Japan
- Position: Intern
- Implementation of a cutting machine simulator in MATLAB from scratch working with a mechanical engineer specialized in cutting engineering.
- Analysis on smoothness of cut surfaces with combinations of certain materials and cutting edges using the simulator.
Toyota Technological Institute
- Period: April 2016 - April 2017
- Degree: Studied towards M.A. in Computer Science (Not Completed)
- Further research on Deep Learning application in text classification.
- Research on question answering by Deep Learning at TTI At Chicago for 3 months.
Toyota Technological Institute
- Period: April 2012 - March 2016
- Degree: B.A. in Mechanical Engineering and Computer Science
- Research on applying Deep Learning to text classification for languages with ideograms (e.g. Kanjis in Japanese) combining convolutional and recurrent neural networks.
- Implementation and development of Deep Learning models using Python, TensorFlow, and Chainer.
- Development of data processing/analysis pipelines for Deep Learning models.
- Shell script
Frameworks / Runtimes
- React / React Native
- RESTful API
- Google Cloud Platform