Job description

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 • 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 The Team Deloitte’s Government & Public Services (GPS) practice – our people, ideas, technology and outcomes – is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise. Our Engineering as a Service offering provides end-to-end design, implementation, and technology operations, leveraging our core engineering expertise. We help transform engineering teams, modernize technology, & deliver complex programs with a product engineering mindset. Our flexible delivery models— traditional teams, pools, or pods, are tailored for each client’s needs, offering engineering-led Advise, Implement, & Operate capabilities to accelerate innovation. This opportunity sits within our Deloitte US Delivery Center model, which is dedicated to driving impactful business services. It leverages Deloitte’s scale and talent, as well as a center delivery model to provide high-quality, cost-effective service with standardized processes and procedures to service businesses across Deloitte. The Deloitte US Delivery Center has a small-business feel with a big-business impact. With the resources of Deloitte and a community feel, the delivery center model provides high-quality services to our clients. USDC professionals work out of one of our specific delivery center locations, and each location presents dynamic career opportunities for professionals to focus on their work with nominal travel requirements. Qualifications Required : • 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 Preferred: • 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

Requirements

Must-haves

Work You’ll Do
As a US Delivery Center Consultant - Infrastructure Engineer on the team, 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
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
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
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)

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningawsmlci/cdpythonsql

Platform: avature
Effort: medium