ML Ops Architect

Pay Rate : $53 - $58 / Hour

Overview :

We are seeking a talented AWS DevOps / MLOps Architect to develop platforms for big data and data science on AWS. As models, apps, and data pipelines are created and operationalized, the bigdata and data science team requires engineers with understanding of cloud native technology to develop, manage, automate, and facilitate the operational capabilities of the big data and data science team.

Required Skills :

• 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 Client 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.

Required Skill-set :

Qualifications :