Engineer, Data Scientist
Summary
On-site data scientist/engineer in Kuching, Malaysia who builds and maintains ML, deep learning, generative-AI, and agentic-AI systems to optimize manufacturing processes. Day-to-day includes Python model development, data pipelines, deployment with Docker/Kubernetes, and production model monitoring.
We are seeking an Engineer, Data Scientist to join our team in Kuching, Sarawak. This is a full-time position that plays a crucial role in leveraging data analytics and scientific methodologies to drive operational efficiency, optimize manufacturing processes, and support strategic decision-making across our organization.
What you’ll be doing
Research, design, and implement machine learning (ML), deep learning, generative AI, and agentic AI models and systems.
Maintain and enhance existing ML frameworks, pipelines, and libraries.
Ensure data quality through preprocessing, cleaning, and validation techniques.
Conduct applied research to support project-specific requirements in data science, ML algorithms, and automation.
Monitor and evaluate model performance in production environments, ensuring accuracy and reliability.
Document system architecture, model behavior, and deployment processes for both technical and non-technical stakeholders.
Facilitate User Acceptance Testing (UAT) and manage deployment to production environments.
Use Git for version control and Docker for containerization and deployment.
Develop reusable, modular, and well-documented Python packages.
What we’re looking for
Bachelor’s degree or higher in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field.
Minimum 2 years of hands-on experience in data science or AI engineering roles (preferred).
Strong proficiency in Python and object-oriented programming (OOP).
Solid understanding of data structures, data modeling, ETL processes, and model evaluation techniques.
Familiarity with libraries / frameworks such as TensorFlow, PyTorch, Keras, scikit-learn, LightGBM, transformers, LLMs, and Plotly.
Experience with generative AI (VLLM, guardrails, RAG, etc.), agentic AI, computer vision, global optimization, and time-series forecasting is a plus.
Working knowledge of Git, CI/CD pipelines, and MLOps practices.
Experience with Linux environments and tools like Python Dash, Streamlit, Docker, and Kubernetes.
Basic knowledge of SQL, NoSQL, graph, and/or vector databases (e.g., PostgreSQL, MongoDB, Qdrant, Neo4j, ArcadeDB).
Skills
- Agentic AI
- AI
- Analytics
- Automation
- CI/CD
- Computer Vision
- Containerization
- Data Analytics
- Data Modeling
- Data Quality
- Data Science
- Deep Learning
- Docker
- ETL
- Generative AI
- Git
- Keras
- Kubernetes
- Linux
- LLM
- Machine Learning
- MLOps
- Model Evaluation
- MongoDB
- Neo4j
- NoSQL
- OOP
- Plotly
- PostgreSQL
- Python
- PyTorch
- Qdrant
- scikit-learn
- SQL
- Streamlit
- TensorFlow
- Time Series
- Transformers
- Vector Databases
- Version Control
- vLLM