About
Software Engineer with 2.5+ years building production AI/ML and backend systems in Python, Jac, and Go: LLM-powered agentic applications, MLOps pipelines, and scalable infrastructure. Shipped AI products used by real customers, including live apps on Google Play and the App Store. Open-source core contributor to the Jaseci stack and co-author of two IEEE-published papers on deep learning.
Experience
AI/ML Engineer · Jaseci Lab
Apr 2024 – Present
Ann Arbor, MI, USA · Remote
- Core contributor to the open-source Jaseci stack (950+ GitHub stars) with 70+ merged PRs, as a core developer of the jac-scale Kubernetes deployment and scaling features and contributor to the jaclang language.
- Developed and maintained backend infrastructure for Tobu, an AI-powered personal memory platform with integrated LLMs serving 1,000+ users; took the system to production fast enough to support the company's $1.5M raise.
- Shipped ML features for image analysis and conversation processing that turn unstructured photos and chats into structured, searchable information, and built scalable PySpark pipelines for user analysis and feature optimization.
- Lead developer of Flowline, an AI-powered project management platform deployed on Kubernetes, and built the Veo 3 image-to-video pipeline behind TLite, live on the App Store.
PythonJacKubernetesLLMsPySparkFastAPIData Scientist · Utech Technologies (Pvt) Ltd
Apr 2024 – Jun 2024
Wattala, Sri Lanka · Remote
- Designed and deployed an end-to-end energy forecasting pipeline with MLflow experiment tracking and model versioning for reproducible, production-ready forecasts.
- Automated ML workflows with Apache Airflow for data ingestion, model retraining, and deployment.
- Built production API services with FastAPI and Docker for real-time energy prediction and dashboard visualization.
MLflowApache AirflowFastAPIDockerPythonApplication Engineer Intern · Synopsys Lanka (Pvt) Ltd
Jan 2023 – Jul 2023
Colombo, Sri Lanka · On-site
- Developed an NLP-based search engine with LSTM models for automated email classification.
- Automated large-file compilation and notification systems using Python, Bash, and CSH scripting.
- Managed 17 processor designs through custom automation scripts for daily performance assessment and resource optimization.
PythonBashLSTMNLP
Education
B.Sc. Engineering (Hons) in Electrical Engineering
University of Moratuwa · Jan 2020 – Jul 2024
GPA 3.31 (Second Upper Class) · Washington Accord-accredited · Dean's List (Semester 2)
Skills & technologies
Toolbox
Languages
- Python
- Go
- Rust
- TypeScript
- Jac (jaclang)
- SQL
- Bash
AI & GenAI
- LLMs
- RAG
- Agentic AI
- LangGraph
- Fine-tuning (Unsloth)
- NLP
- Deep Learning
- Veo 3
ML Frameworks
- PyTorch
- TensorFlow / Keras
- scikit-learn
- Hugging Face
MLOps & Infra
- Docker
- Kubernetes
- MLflow
- Apache Airflow
- CI/CD (GitHub Actions)
- Google Cloud
- AWS
Backend & Data
- FastAPI
- Jaseci
- PostgreSQL
- PySpark
- Kafka
- Redis Streams
- WebSockets
- Chroma
Publications
Peer-reviewed work on self-adaptive deep learning for non-intrusive load monitoring.
Research
2024 IEEE World AI IoT Congress · 2024
Self-Adaptive Non-Intrusive Load Monitoring Using Deep LearningM. S. K. Nisakya, S. M. L. Arampola, W. A. Yasodya, S. Kumarawadu, V. Logeeshan, C. Wanigasekara
Introduces a self-adaptive NILM model integrating deep learning with transfer learning and pseudo-labeling, so energy-monitoring models keep working as appliance power patterns drift with age.
Best Presented Paper AwardIEEE Access, vol. 13, pp. 106524–106539 · 2025
Self-Adaptive Deep Learning Framework for Non-Intrusive Load Monitoring: Addressing Aging Appliance Challenges With Transfer Learning and Pseudo LabelingW. A. Yasodya, S. M. L. Arampola, M. S. K. Nisakya, V. Logeeshan, S. Kumarawadu, C. Wanigasekara
Journal extension of the self-adaptive NILM framework, with synthetic data generation and advanced neural architectures achieving high disaggregation accuracy on aging appliances.
GitHub activity, live
Fetched from the GitHub GraphQL API and revalidated hourly. These are not hand-typed numbers.
By the numbers
Live from the GitHub API
2,283
Contributions
past 12 months
3,104
Commits
all-time
406
Pull Requests
all-time
29
Public Repos
on GitHub
2
IEEE Publications
peer-reviewed
2.5+
Years of Experience
and counting
Last 12 months of GitHub contributions: 2,283 total