Machine Learning Ai Operation

1 day ago

Kuala Lumpur, Malaysia IT Consulting Company Full time

Key Responsibilities
- Model Deployment: Automate and streamline the process of deploying machine learning models into production environments, ensuring they run reliably at scale.
- Pipeline Construction: Build and maintain robust data pipelines for continuous training and deployment of machine learning models, including Generative AI models such as LLMs (Large Language Models).
- Monitoring and Maintenance: Implement monitoring solutions to track the performance and health of models in production, quickly identifying and addressing degradation or failures.
- Versioning and Experiment Tracking: Manage version control of both data and models. Use tools like MLflow or DVC to track experiments, manage the lifecycle of machine learning models, and ensure reproducibility.
- Collaboration: Work closely with data scientists, AI researchers, and software engineers to ensure that ML systems are well-integrated with the company’s software infrastructure.
- Performance Optimization: Optimize machine learning infrastructure for performance and cost, utilizing cloud technologies and services efficiently.
- Regulatory Compliance: Ensure that the machine learning deployments comply with relevant data privacy and protection regulations, particularly when handling sensitive or personal data.

**Requirements**:

- At least 2 years+ experience in related field.
- Programming Skills: Proficiency in Python is typically essential, with knowledge of libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn.
- DevOps Tools: Experience with Docker, Kubernetes, and CI/CD tools (e.g., Jenkins, GitLab CI) for automation and orchestration.
- Data Management: Knowledge of handling big data technologies and databases (e.g., Hadoop, Spark, MySQL, MongoDB).
- Monitoring Tools: Familiarity with monitoring tools like Prometheus, Grafana, or ELK stack to track system performance and health.

**Job Type**: Permanent

Pay: From RM5,500.00 per month

Schedule:

- Monday to Friday

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