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# Infrastructure Engineer (AWS/ML) - Delivery Consultant, Software Engineering Solutions

- **Company:** Deloitte T.T.L.
- **Location:** Lake Mary, FL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Microsoft Access, Amazon Web Services, Continuous Integration, Information Engineering, Data Security, DevOps, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Performance Tuning, Release Management, Standard Sql, Software Engineering, Management of Software Versions, Data Logging, Amazon Virtual Private Cloud (VPC), Containerization, Git Flow, Infrastructure Automation Frameworks, Deployment Automation, Machine Learning Operations, Cloudwatch, Software Version Control
- **Published:** September 2, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3367839043&tx=KL5757FFI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree
 * 2+ years of hands-on AWS, DevOps, and infrastructure experience
 * 2+ years of experience developing with Python and SQL
 * Experience deploying and supporting ML pipelines and production models, including monitoring, troubleshooting, versioning, rollback, and release management
 * Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
 * Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
 * Delivery Center Location & Travel Requirements:
 * Hybrid Work Model: Operate under a hybrid system requiring residence within a commutable distance to one of the US Delivery Center locations (Gilbert, Lake Mary or Mechanicsburg)
 * Co-location Expectation: Spend up to 30% of working time co-located at an assigned office for orchestrated opportunities, including projects, practice sessions, training, and Moments That Matter at a Deloitte Delivery Center location, Geo-Hub location, approved site, or project location
 * Travel Requirement: Maximum of 10% overnight travel for client or project purposes
 * Relocation Requirement: If relocation is necessary, complete the move within 12 weeks from the start date to reside within a commutable distance, * Strong written and verbal communication skills, with the ability to explain technical issues to both engineering and business stakeholders.
 * Hands on AWS experience - compute, storage, IAM, VPC/networking, security groups, SageMaker, Glue, Athena, CloudTrail, CloudWatch, IaaC, and environment management
 * Experience implementing DevOps / pipeline engineering: CI/CD, GitHub, infrastructure as code, containerization, deployment automation on AWS

## Description

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., * 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
 * Stand up and maintain research and production environments for analytics and ML pipelines
 * Implement secure data access, environment configuration, deployment readiness, and operational controls
 * Provide production support through logging, alerting, incident response, root-cause analysis, job recovery, troubleshooting, and performance tuning
 * Troubleshoot basic AWS networking, platform, and deployment issues
 * Embed security, access, and compliance controls into platform operations and deployment processes
 * Collaborate across infrastructure, application, data engineering, and data science teams while maintaining ownership of assigned deliverables and platform reliability
 * Document technical processes, operating procedures, deployment standards, and support requirements


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