Job Description

Senior AWS Cloud Engineers (ust Have MLOps and Data Analytics Exp)

Office locations (Pittsburgh, PA, Cleveland, OH, Dallas, TX, Birmingham, AL or Phoenix, AZ,).

Technical Skills:
o Amazon SageMaker: In-depth knowledge of SageMaker, including domain setup, configuration, and infrastructure management.
o Cloud Knowledge: A deep understanding of cloud computing concepts, especially related to Amazon Web Services (AWS).
o Infrastructure Design: Ability to design and implement MLOPs cloud solutions, considering scalability, security, and performance.
o Experience: Practical firsthand experience with cloud MLOps and Data Analytics platforms, preferably AWS SageMaker, Glue, EMR, Athena.
o Best Practices: Familiarity with best practices for MLOps and Data Engineering.
o EC2 Instances: Understanding of EC2 instance types and their suitability for AWS SageMaker.
o S3: Proficiency in using Amazon S3 for data storage and SageMaker input/output.
o IAM: Ability to manage permissions and access control using Identity and Access Management.
o Lambda: Knowledge of serverless computing for automating tasks.
o ML & Data Pipelines: Experience with creating data pipelines using AWS SageMaker services integrated with Glue and EMR.
o Monitoring and Troubleshooting: Proficiency in monitoring SageMaker cluster health, identifying bottlenecks, and resolving issues.
o Cost Optimization: Strategies to tag SageMaker resources with an eye on optimizing costs and observability.

Experience and Certifications:
o Experience: Senior AWS Cloud Engineers must have 3 to 5 years of firsthand experience in designing and building cloud MLOps and Data Analytics applications.
o Certifications: AWS Professional Developer or Data certifications are desired for senior roles

MLOps Engineer
LOCATION: Pittsburgh, PA, Cleveland, OH, Dallas, TX, Birmingham, AL or Phoenix, AZ.
Its hybrid and 3 days onsite

Technical Skills:
o Amazon SageMaker: In-depth knowledge of SageMaker, including domain setup, configuration, and infrastructure management.
o Cloud Knowledge: A deep understanding of cloud computing concepts, especially related to Amazon Web Services (AWS).
o Infrastructure Design: Ability to design and implement MLOPs cloud solutions, considering scalability, security, and performance.
o Experience: Practical firsthand experience with cloud MLOps and Data Analytics platforms, preferably AWS SageMaker, Glue, EMR, Athena.
o Best Practices: Familiarity with best practices for MLOps and Data Engineering.
o EC2 Instances: Understanding of EC2 instance types and their suitability for AWS SageMaker.
o S3: Proficiency in using Amazon S3 for data storage and SageMaker input/output.
o IAM: Ability to manage permissions and access control using Identity and Access Management.
o Lambda: Knowledge of serverless computing for automating tasks.
o ML & Data Pipelines: Experience with creating data pipelines using AWS SageMaker services integrated with Glue and EMR.
o Monitoring and Troubleshooting: Proficiency in monitoring SageMaker cluster health, identifying bottlenecks, and resolving issues.
o Cost Optimization: Strategies to tag SageMaker resources with an eye on optimizing costs and observability.


Experience and Certifications:
o Experience: Senior AWS Cloud Engineers must have 3 to 5 years of firsthand experience in designing and building cloud MLOps and Data Analytics applications.

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