RESPONSIBILITIES:

As MLOps Engineer, you will work collaboratively with Data Scientists and Data engineers to deploy and operate advanced analytics machine learning models. You ll help automate and streamline Model development and Model operations. You ll build and maintain tools for deployment, monitoring, and operations. You ll also troubleshoot and resolve issues in development, testing, and production environments.
Enable Model tracking, model experimentation, Model automation Develop scalable ML pipelines
Develop MLOps components in Machine learning development life cycle using Model Repository (either of): MLFlow, Kubeflow Model Registry
Machine Learning Services (either of): Kubeflow, DataRobot, HopsWorks, Dataiku or any relevant ML E2E PaaS/SaaS
Work across all phases of Model development life cycle to build MLOPS components
Build the knowledge base required to deliver increasingly complex MLOPS projects on the Cloud(AWS, Azure, GCP)/On Prem Be an integral part of client business development and delivery engagements across multiple domains.

QUALIFICATIONS: REQUIRED QUALIFICATIONS:

5-10 years experience building production-quality software
Strong experience in System Integration, Application Development or DataWarehouse projects across technologies used in the enterprise space
Basic Knowledge of MLOps, machine learning and docker
Object-oriented languages (e.g. Python, PySpark, Java, C#, C++ )
Experience developing CI/CD components for production ready ML pipeline.
Database programming using any flavors of SQL
Knowledge of Git for Source code management
Ability to collaborate effectively with highly technical resources in a fast-paced environment
Ability to solve complex challenges/problems and rapidly deliver innovative solutions
Team handling, problem solving, project management and communication skills creative thinking
Foundational Knowledge of Cloud Computing either one AWS, Azure or GCP
Hunger and passion for learning new skills

Senior Engineer_ MLOPs