MLOps Engineer

Role & responsibilities

As an ML Engineering Specialist you will be owning responsibility to operationalize ML models NLP Computer Vision and other type of models

• End to End model lifecycle management starting from feature extraction to monitor machine learning models using high end tools and technologies
• Design implementation of DevOps principles in Machine Learning
• Model quality assurance governance and monitoring
• Integrate models as part of business applications via APIs
• Execute best practices in version control and continuous integration delivery
• Collaborate with data scientists engineers and other key stakeholders
• Work well in a fast-paced cross functional environment

Mandatory Skills Requirements

• Experience in implementing machine learning life cycle on Azure ML and Azure Databricks along with other Azure services such as Azure DevOps Azure functions etc
• Experience with Machine learning frameworks libraries and agile environments
• Experience implementing Azure Cognitive services in business applications
• Experience with various model deployment strategies
• Experience with Python and SQL must understand of distributed frameworks such as Spark Dask Ray etc is a plus
• Experience with version control tools such as Git Bitbucket etc
• Knowledge on Docker Jenkins Kubernetes and other DevOps tools
• Knowledge on Infra as a Code via ARM or Terraform templates
• Familiarity with Large Language Models and Operationalization of foundation models on cloud platforms will be a plus
• Outstanding analytical and problem-solving skills.