Ml Ops Specialist, Ai/ml

6 months ago


Malaysia NodeFlair Full time

**Job Summary**:
**Job Type**

**Seniority**

**Years of Experience**
Information not provided

**Tech Stacks**
Container GitLab Amazon S3 AWS Docker Jenkins GitLab CI Kubeflow CI ELK EC2 Git Azure Java Grafana Prometheus Kubernetes Scala Python

**_Job Responsibilities:_**
- Building and maintaining the infrastructure necessary for deploying, monitoring, and scaling machine learning models within the bank.
- Develop and maintain pipelines for model training, evaluation, and deployment.
- Implement monitoring and alerting systems to track model performance and data quality in production.
- Automate processes for model retraining, versioning, and rollback.
- Collaborate with data scientists and software engineers to optimize models for performance, scalability, and reliability.
- Ensure compliance with regulatory requirements and security standards in all ML operations.
- Research and implement best practices for ML Ops to improve efficiency and reduce operational overhead.

**_
Job Requirement:_**
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Familiarity with machine learning concepts and techniques.
- Strong understanding of cloud computing platforms and containerization technologies.
- Proven experience in software development and/or data engineering.
- Experience with deploying and managing machine learning models in production environments.
- Proficiency in programming languages such as Python, Java, or Scala.
- Knowledge of DevOps principles and practices.
- Proficiency in cloud platforms like AWS, Azure, or GCP.
- Experience with containerization tools like Docker and Kubernetes.
- Knowledge of CI/CD pipelines and automation tools like Jenkins or GitLab CI.
- Familiarity with version control systems such as Git.

**_
Required Tool and Program Proficiency:_**
- Proficient in cloud services such as AWS S3, EC2, SageMaker, or equivalent.
- Experience with container orchestration platforms like Kubernetes.
- Familiarity with monitoring and logging tools such as Prometheus, Grafana, or ELK stack.
- Knowledge of ML Ops platforms like Kubeflow or MLflow is a plus.



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