CV
Basics
| Name | Sabal KC |
| Label | Full-Stack / Backend Software Engineer |
| sabalkc462@gmail.com | |
| Url | https://sabalkc.com.np |
| https://linkedin.com/in/sabal-kc/ | |
| Github | https://github.com/sabal-kc |
| Summary | Full-stack and backend software engineer with experience building Go microservices, browser automation systems, data reporting platforms, React applications, and AWS-backed AI/ML projects. |
Work
- 2024.09 - Present
Research Assistant (Software Developer)
University of Houston, Office of Institutional Research
Develop centralized reporting and data extraction tools for institutional assessment workflows.
- Developed a full-stack centralized reporting platform using HTML, CSS, JavaScript, and PHP to automate annual assessment reporting for 200+ degree programs.
- Modernized reporting infrastructure by building a KPI extraction pipeline in SQL and PHP, replacing manual reporting of metrics and ensuring 100% data accuracy from Oracle tables in the Data Warehouse.
- 2021.05 - 2024.07
Software Engineer
Dapi
Built backend services, browser automation infrastructure, SDKs, and deployment pipelines for an open-banking ecosystem.
- Engineered and maintained backend services using Go (GoKit, Fiber) and MongoDB, utilizing NATS and Protocol Buffers for low-latency service communication.
- Created and scaled an in-house browser-as-a-service system using Chromium, Node.js, Docker, and Kubernetes, achieving 90% cost reduction by eliminating third-party services.
- Led a team of 4 engineers to develop and maintain web scraping and automation scripts in TypeScript, Puppeteer, and Node.js for open banking integrations across 30+ banks in the UAE and US, achieving a 95% success rate.
- Managed client libraries and SDKs in Python, PHP, and Node.js and maintained comprehensive documentation, reducing client integration time.
- Implemented React frontend features for the client dashboard and built CI/CD pipelines using GitHub Actions and Porter.
- 2020.12 - 2021.04
Full-Stack Developer Intern
Okhati Solutions
Developed EMR software used by clinics, labs, and hospitals.
- Developed and maintained an EMR platform using React, Redux, Node.js, Express, and PostgreSQL to streamline clinical and laboratory data management.
- Led integration work for government COVID-19 data reporting, enabling tracking for 1000+ users daily.
Education
-
2024.08 - 2026.05 Master of Science (M.S.)
University of Houston
Computer Science
- Artificial Intelligence, Cloud Computing, Database Systems
Agg. score: N/A
-
2016.11 - 2021.04 Bachelor of Engineering (B.E.) in Computer Engineering
Pulchowk campus, IOE, Tribhuvan University
Computer Engineering
- Software engineering, Operating system, Object oriented programming, Data structures and algorithms, AI, Mathematics
Agg. score: 73.6% (First division)
Publications
-
2026.04 Integrating InSAR and Channel Steepness for AI-Based Coseismic Landslide Modeling in the Nepal Himalaya
Remote Sensing
Co-authored an MDPI Remote Sensing article integrating InSAR-derived displacement, channel steepness, and machine learning/deep learning models for coseismic landslide probability mapping in the Nepal Himalaya.
Skills
| Programming languages | |
| Go (Golang), JavaScript, TypeScript, Python, PHP |
| Backend & APIs | |
| Node.js, Express, Flask, Supabase, Microservices, REST APIs |
| Databases | |
| Oracle, MongoDB, PostgreSQL |
| Cloud & Containerization | |
| AWS (EC2, S3, ECS, Fargate, SageMaker), Docker, Kubernetes, CI/CD |
| Developer Tooling & Automation | |
| Git, Web scraping and browser automation (Puppeteer, Playwright), Postman |
Projects
-
Moodle/Canvas AI Agent
LangGraph-based AI agent that automates LMS login, assignment discovery, and assignment submission via Playwright and an agentic tool loop.
- Compared LLMs for login workflows across Moodle and Canvas using cost, token usage, and latency measurements.
- Deployed as a containerized long-running service on AWS with scheduled execution and logging through LangSmith.
-
AWS Cloud-Based Movie Recommendation System
Scalable personalized movie recommender built with Go microservices, React, AWS, SageMaker ML models, and LLM-powered natural-language recommendations.
- Built a cloud-native architecture with AWS Amplify, Cognito, API Gateway, ALB, ECS, Fargate, DynamoDB, S3, SageMaker, ECR, and CloudWatch.
- Containerized backend services with Docker and compared ECS on Fargate and EC2 for cost and performance trade-offs.