Description

JD:

•            Experience in AWS system and network architecture design, with specific focus on AWS Sagemaker and AWS ECS
•            Experience developing and maintaining ML systems built with open source tools
•            Experience developing with containers and Kubernetes in cloud computing environments
•            Experience with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo)
•            Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
•            Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
•            Support model development, with an emphasis on auditability, versioning, and data security
•            Experience with data security and privacy solutions such as Denodo, Protegrity, and synthetic data generation.
•            Ability to develop applications using Python and deploy to AWS Lambda and API Gateway
•            Ability to develop Jenkins pipelines using the groovy scripting.
•            . Good understanding in testing frameworks like Py/Test.
•            Ability to work with AWS services like S3, DynamoDB, Glue, Redshift and RDS
•            Proficient understanding of Git and version control systems
•            Familiarity with continuous integration and continuous deployment.
•            Develop the terraform modules to deploy the standard infrastructure.
•            Ability to develop the deployment pipelines using the Jenkins, XL Release
•            Experience in Python boto3 to automate the cloud operations.
•            Experience in documenting technical solutions and solution diagrams
•            Good understanding of the simple python applications which can be deployed as a docker container.
•            Experiencing in creating workflows using AWS step functions
•            Create the docker images using the custom PYTHON libraries

Education

Any Graduate