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.