[Onsite Sing] AI Software Engineer - Full Stack

Scope of work
We are looking for a highly skilled AI Software Engineer - Full Stack to design, develop, and deploy AI/ML-powered industrial applications. The role involves building robust backend services, intuitive frontend interfaces, and seamless integration with industrial systems such as OPC-UA servers, historians, and MES platforms. You will work closely with AI/ML engineers, process domain experts, and system architects to deliver scalable, secure, and production-ready AI solutions for process industries (oil & gas, chemicals, water, energy, manufacturing).
Location: Primarily work from the ODC room together with the current project team in Hanoi. Willingness to relocate or work onsite in Singapore depending on client requirements.
Requirements
Must have:
Overall Experience: 4 -10 years of software development experience. The JD is flexible depending on skill depth. Candidates with around 4 years of experience may also be considered if they have strong Full-stack skills and hands-on experience applying AI/ML in applications.
English: Intermediate level or above, with the ability to read and write technical documentation and communicate effectively in a working environment.
Python (Backend + AI): Proficient in Python for both backend development and AI application development.
Backend & API: Experience designing and implementing REST APIs using FastAPI, Flask, or Django. Hands-on experience with data ingestion and pressing pipelines for both real-time and historical data, as well as authentication/authorization, logging, and error handling. Able to optimize performance, scalability, and reliability for production environments.
Frontend: Experience designing and developing responsive, user-friendly frontend interfaces using React, Angular, or Vue. Able to build dashboards and visualizations for AI predictions, KPIs, trends, and alerts, and integrate frontend applications with backend APIs and AI services. Familiarity with cross-browser compatibility and usability requirements for industrial users.
Data & Realtime: Knowledge of REST/JSON and WebSockets, with experience handling time-series data and working with relational and/or NoSQL databases.
AI/ML Application Development: Hands-on experience with ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow/Keras, and time-series libraries such as Prophet, Darts, or GluonTS. Experience integrating trained ML models into backend systems for inference, scoring, or analytics. Understanding of model lifecycle management, including model versioning, deployment, monitoring, and retraining.
Software Engineering & Git: Solid understanding of software architecture, design patterns, clean code principles, version control with Git, and collaborative software development.
Docker / Deployment: Working knowledge of Docker and containerized application deployment.
Testing: Ability to write and maintain unit tests and integration tests.
Soft Skills: Strong problem-solving and analytical skills. Ability to work effectively in cross-functional and multidisciplinary teams. Good communication skills when collaborating with AI, domain, and OT teams. Self-driven with a strong sense of ownership.
Education: Bachelor's degree in Computer Science, Software Engineering, or a related field.
Nice-to-have:
MLOps: Familiarity with MLOps tools and practices, including MLflow/Kubeflow, model monitoring, and drift detection.
Industrial Systems Integration: Experience integrating AI applications with industrial systems, including OPC-UA servers, process historians such as PI and Exaquantum, and MES/SCADA/DCS systems. Ability to handle time-series data, event data, and metadata from OT systems, while ensuring secure and reliable OT-IT data exchange.
Message Brokers: Knowledge of messaging and event-streaming technologies such as MQTT, Kafka, and RabbitMQ.
Data Visualization Libraries: Experience with data visualization libraries such as [protected info], Plotly, or [protected info] 5. Domain: Experience in process industries such as oil & gas, chemicals, water, energy, or manufacturing, or in OT-IT integration.