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Ml Ops Specialist, Ai/ml

3 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.