Location : Johor, Malaysia

Duration: 12 Month extendable contract

Experience: 5-8 years

Visa will be sponsored( should be able to relocate)

Key Responsibilities :

ML System Development & Deployment :

- Develop, deploy, and maintain end-to-end machine learning systems using Python.

Containerization & Orchestration :

- Package and manage ML applications using containerization tools like Docker and Podman.

- Orchestrate these containers for large-scale deployment and management with platforms such as Kubernetes or Docker Swarm.

CI/CD Pipeline Management :

- Design and implement continuous integration and continuous deployment (CI/CD) pipelines for ML models using tools like Git, Jenkins, and GitHub Actions.

Monitoring & Logging :

- Establish comprehensive monitoring and logging strategies for ML models in production to ensure performance, stability, and data integrity using tools like ELK Stack, Prometheus, and Telegraf.

Data Streaming & Integration :

- Work with data streaming platforms such as Apache Kafka, Flink, and RabbitMQ to build real-time data pipelines for model training and inference.

Infrastructure & Configuration Management :

- Utilize configuration and infrastructure tools like Ansible, Puppet, or SaltStack to automate the setup and management of the ML infrastructure.

Database Management :

- Interact with and manage various databases, including relational (e.g , PostgreSQL, MySQL) and NoSQL (e.g , MongoDB, Redis), to support ML workflows.

Model Serving & API Development :

- Deploy and serve trained AI models using specialized frameworks like TensorFlow Serving, ONNX Runtime, or Nvidia Triton, and develop robust API services with FastAPI and Streamlit.

Education :

- A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Applied Mathematics, Physics, or a related technical field.

- Equivalent hands-on experience in AI/ML engineering, DevOps, or systems architecture may also be considered.

Required Experience :

- Experience in developing and deploying machine learning systems using Python, containerization tools like Docker and Podman, and Linux-based operating systems such as Ubuntu or RHEL.

- Experience with orchestration platforms like Kubernetes or Docker Swarm, and CI/CD tools such as Git, Jenkins, and GitHub Actions.

- Proficiency in monitoring and logging tools such as ELK Stack, Fluentd, Prometheus, Telegraf, and various data streaming platforms like Apache Kafka, Flink, Storm, and RabbitMQ.

- Practical knowledge of relational and NoSQL databases such as PostgreSQL, MariaDB, MySQL, MongoDB, Redis, and InfluxDB.

- Hands-on experience with AI/ML frameworks like TensorFlow, PyTorch, Transformers, Scikit-learn, Ollama, LangChain, and CrewAI.

- Familiarity with configuration and infrastructure tools including Ansible, Puppet, SaltStack, as well as visualization libraries such as Grafana, Kibana, Matplotlib, and Plotly.

- Working knowledge of AI model deployment frameworks such as TensorFlow Serving, ONNX Runtime, TorchServe, Nvidia Triton, and API services using FastAPI and Streamlit.

Certifications (Preferred) :

- AWS Certified Machine Learning - Specialty

- Certified Kubernetes Administrator (CKA)

- TensorFlow Developer Certificate

- Microsoft Azure AI Engineer Associate

- Certified MLOps Engineer from recognized training platforms (e.g, Coursera, DataCamp, Udacity)