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Infrastructure Engineer (AWS/ML) - Delivery Consultant, Software Engineering Solutions
Deloitte US
Gilbert, USA
Full-time
Entry · 2+ years experience
Hybrid
Discovered Yesterday
AWSIAMVPCSageMakerGlueAthena
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Role Overview
Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients’ success. You’ll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
Key Skills for This Role
AWSIAMVPCSageMakerGlueAthena
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Position Summary
Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients’ success. You’ll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
Work You’ll Do
As a US Delivery Center Consultant - Infrastructure Engineer on the team, you will:
You will help operationalize and support the machine learning lifecycle in AWS-based environments. You will work with data engineering, data science, infrastructure, and application teams to deploy, manage, monitor, and support production ML solutions.
You will:
Build and maintain AWS environments supporting analytics and ML workloads, including compute, storage, IAM, VPCs, security groups, SageMaker, Glue, Athena, CloudTrail, and CloudWatch
Support ML pipeline engineering, including Git workflows, CI/CD, infrastructure as code, containerization, and deployment automation
Deploy and manage ML models in production, including endpoints, model versioning, rollback procedures, and release controls
Support MLflow-enabled experiment tracking and model lifecycle management
Execute shadow testing and parallel-run validation to compare new models or pipelines with current-state solutions, identify drift, and assess production readiness
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