Job Description

Responsibilities:

• Develop, deploy and maintain machine learning models, pipelines and workflows in production environment.
• Build and maintain machine learning infrastructure that is scalable, reliable and efficient.
•  Collaborate with data scientists and data engineers to design and implement machine learning workflows.
• Implement monitoring and logging tools to ensure that machine learning models are performing optimally.
•  Continuously improve the performance, scalability and reliability of machine learning systems.
• Work with infrastructure team to deploy and manage infrastructure for machine learning services.
• Create and maintain technical documentation for machine learning infrastructure and workflows.

Desirable Skills:

•  Bachelor's or Master's degree in computer science, engineering or related field.
•  5+ years of experience in software development, machine learning engineering or related field.
• Strong understanding of machine learning concepts and frameworks, including TensorFlow, PyTorch, Scikit-learn, etc.
•  Experience with ML Ops in Azure preferred, including Databricks MLFlow.
•  Experience with Databricks and Google Cloud Platform
• Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git.
•  Experience in developing and deploying machine learning models in a production environment.
•  Preferably with cloud technologies such as Azure and Google
• Preferably with data streaming technologies such as Kafka, Kinesis, etc.
• Preferably with MLOps platforms such as Kubeflow, MLFlow, Azure ML etc.