I’m a Machine Learning Engineer with experience in search, recommendation, and AI systems.
My work spans machine learning, NLP, graph neural networks, generative AI, and scalable backend engineering.
I enjoy building end-to-end ML systems — from data pipelines to production deployment — with a focus on high-performance, real-world impact.
Programming Languages:
- Python (Data, ML, Backend)
- Golang (Backend, Web Services)
- R (Data Analytics)
- Java (Backend, Data)
- Scala (Data Pipelines)
Machine Learning & AI:
- BERT, Fairseq, GPT, Transformer Models
- DALL·E, Stable Diffusion, CLIP, OpenCV
- PyTorch, Sklearn, XGBoost, LightGBM, CatBoost, StatModels
- NLP, Knowledge Graphs, Multimodal AI, Graph Neural Networks
Cloud & Infrastructure (AWS):
- EC2, EKS, ECS, S3, RDS, Lambda, Kinesis, Firehose, CloudWatch
- SageMaker, MWAA (Airflow), Athena, CloudFormation, Lake Formation, Bedrock
Databases:
- Relational: Postgres, MySQL, MariaDB, OracleDB, Greenplum, Teradata
- Vector: ElasticSearch, Qdrant, Weaviate
- NoSQL: Redis, MongoDB, HBase, Cassandra, InfluxDB, Kylin
Data Visualization:
- Tableau, Altair, Matplotlib, Seaborn, ggplot, D3.js
DevOps & Tools:
- Git, Docker, CI/CD, YAML, ProtoBuf, Conda
- M.Sc. Data Science — University of British Columbia
- M.Sc. Information Systems Management — Carnegie Mellon University
- B.Sc. Industrial Management (MIS) — Purdue University
I’ve published on recommendation systems, NLP, sentiment analysis, graph neural networks, and data privacy/security in AI.
👉 Google Scholar Profile
✨ Always exploring the intersection of AI research and production engineering.