AWS AI & DevOps Intern
About The Position
Do you want to gain exposure to IT and DevOps projects associated with AI? Are you great at collaborating with team members to creatively problem solve? Do you want to work for a Chicago’s Best & Brightest Company to Work For? Does working in a highly-engaged organization , one that’s committed to growth, collaboration and innovation interest you? IF SO, READ ON. This is an in person internship where you will gain hands-on experience in an office setting! The AWS AI Developer Intern will support the IT and DevOps team in designing, developing, and deploying artificial intelligence and machine learning solutions on Amazon Web Services (AWS). This role offers hands-on experience with cloud-native AI/ML services, infrastructure automation, and real-world business applications of generative AI. The intern will work closely with senior engineers, the Associate DevOps Engineer, and cross-functional stakeholders to build intelligent solutions that drive operational efficiency and innovation.
Requirements
- Currently enrolled in an associate's or bachelor's degree program in Information Technology, Computer Science, or a related field.
- Proficiency in one or more programming languages such as Python (preferred), JavaScript/TypeScript, Java, C#, Go, or Rust.
- Experience with version control (Git/GitHub) and basic command-line/Linux operations.
- Strong analytical and problem-solving abilities with attention to detail.
- Excellent written and verbal communication skills.
- Self-motivated with the ability to manage time effectively and work independently.
- Collaborative team player with a growth mindset and eagerness to learn.
Nice To Haves
- Experience with Python ML/AI libraries (pandas, NumPy, scikit-learn, PyTorch, or TensorFlow) is strongly preferred.
- Foundational understanding of machine learning concepts: supervised/unsupervised learning, neural networks, NLP, and computer vision.
- Exposure to AWS cloud services (EC2, S3, Lambda, IAM) or willingness to obtain AWS Cloud Practitioner certification prior to start.
Responsibilities
- AI/ML Development & Deployment: Design, build, and deploy AI/ML models and applications using AWS services such as Amazon SageMaker, Bedrock, Lambda, and Step Functions.
- Develop and integrate generative AI capabilities (e.g., Amazon Bedrock, foundation models) into internal tools and customer-facing applications.
- Build and maintain data pipelines for model training and inference using AWS Glue, S3, and DynamoDB.
- Monitor model performance and implement logging, alerting, and retraining workflows using Amazon CloudWatch and SageMaker Model Monitor.
- Research and evaluate emerging AWS AI/ML services and industry best practices to recommend adoption.
- DevOps Integration & Infrastructure Automation: Assist in creating CI/CD pipelines for ML model deployment using AWS CodePipeline, CodeBuild, and Infrastructure as Code (CloudFormation/Terraform).
- Implement API endpoints and serverless architectures (API Gateway, Lambda) to expose AI/ML model predictions.
- Support automation of repetitive tasks through intelligent workflows combining AI services with existing DevOps tooling.
- Collaborate with the DevOps team on infrastructure provisioning, monitoring, and deployment best practices.
- Assist with AWS cloud environment management including EC2, S3, RDS, Lambda, and VPC as needed.
- Support Infrastructure as Code (IaC) efforts using Terraform, CloudFormation Microsoft 365 Copilot Rollout & AI Integration: Support the planning, configuration, and rollout of Microsoft 365 Copilot across the organization, including user enablement, licensing coordination, and adoption tracking.
- Develop M365 Copilot agents, custom prompts, and Copilot Studio solutions to enhance productivity across departments (e.g., Teams, Outlook, SharePoint, and Power Platform).
- Collaborate with the team to evaluate Copilot usage analytics, gather user feedback, and identify opportunities to expand AI-driven productivity improvements across the business.
- Documentation & Collaboration: Document technical solutions, architecture diagrams, and runbooks for knowledge sharing across the team.
- Participate in code reviews, Agile ceremonies, and team planning sessions.
- Contribute to Confluence knowledge base articles and internal training materials.
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What This Job Offers
Career Level
Intern
Education Level
Associate degree
