Senior Machine Learning Engineer

4 weeks ago


Bengaluru, Karnataka, India Iron Mountain Full time

We are looking for a highly skilled and experienced Machine Learning Engineer to join our Global Technology team at Iron Mountain. The successful candidate will have a strong background in deploying and managing machine learning models and pipelines in cloud environments, with a focus on scalability, reliability, and performance optimization.

The ideal candidate will have a strong understanding of cloud services and containerization technologies, as well as experience with Gitlab CI/CD pipelines and Python. They will also have a strong understanding of machine learning concepts, algorithms, and techniques, as well as experience with data engineering and orchestration engines.

The Machine Learning Engineer will be responsible for designing, implementing, and optimizing machine learning operations solutions within an agile development framework. They will work closely with the Global Product Engineering team to ensure that machine learning models are deployed into production environments, and that they meet the required scalability, reliability, and performance standards.

The successful candidate will have a strong problem-solving skills and the ability to troubleshoot complex issues in machine learning workflows, infrastructure, and deployments. They will also have a strong understanding of security controls and access policies, and will be able to ensure compliance with regulatory requirements and industry standards for data privacy and security.

We offer a competitive salary and benefits package, as well as opportunities for professional growth and development. If you are a motivated and experienced Machine Learning Engineer looking for a new challenge, please apply for this exciting opportunity.

Key Responsibilities:

  • Design, implement, and optimize machine learning operations solutions within an agile development framework.
  • Deploy machine learning models into production environments, ensuring scalability, reliability, and performance.
  • Provision and manage infrastructure resources for model training, inference, and monitoring.
  • Implement CI/CD pipelines for automated testing, building, and deploying machine learning models.
  • Monitor deployed models for performance metrics, data drift, and concept drift.
  • Manage training data and model artifacts, ensuring traceability and reproducibility of model experiments and iterations.
  • Implement security controls and access policies to protect sensitive data and model assets.


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