AI & DevOps Engineering Intern

Revvity•Waltham, MA

3d•Onsite

About The Position

Revvity is seeking a dynamic, self-motivated Enterprise AI & DevOps Engineering Intern to join their team. This program offers a unique opportunity to gain hands-on experience researching, building, and scaling AI/ML solutions. Interns will learn, develop, and own end-to-end applications and deployment pipelines, working at the intersection of full-stack development, cloud infrastructure, and business strategy. The role involves close collaboration with experienced mentors and cross-functional teams to drive innovative projects from inception to production. This is an ideal opportunity for self-starters who love to learn, work with modern tech stacks, and take full ownership of their work.

Requirements

Bachelor's or master's degree in computer science or equivalent
0-2 years knowledge of Full-Stack experience in JavaScript/TypeScript MVC pattern
0-2 years knowledge in DevOps & Automation: Familiarity with automated CI/CD tools, deployment workflows, or Infrastructure as Code (IaC) concepts

Nice To Haves

Previous coursework or practical projects related to DevOps, containers, cloud infrastructure, data engineering, or machine learning application lifecycle management.
Exposure to foundational cloud computing architecture and cloud-native AI platforms.
Professional cloud computing certifications or training (even if currently in progress).

Responsibilities

Complete technical and corporate onboarding, then map enterprise systems, business architecture, and active AI workstreams.
Conduct informational sessions across business units to shadow team members, document operational friction points, and identify opportunities for AI/ML intervention.
Research emerging AI/ML technologies, industry trends, and software testing methods, learning internal software development best practices, coding standards, and core development tools.
Synthesize findings to highlight where generative AI can reduce manual overhead or unlock creative capacity, and provide feedback on optimizing the onboarding experience.
Write, debug, and develop full-stack proof-of-concepts, including user interfaces and web pages, using Python backends and JavaScript frontends.
Formulate context engineering strategies to optimize enterprise data, memory systems, tool outputs, and prompt chaining within the LLM's context window.
Take full ownership of the deployment pipeline, designing, implementing, and maintaining CI/CD pipelines for LLM application deployments.
Write automated testing workflows from scratch.
Spearhead containerization efforts with Docker and learn to orchestrate, configure, and manage cloud-based deployments on AWS (EC2, S3, Lambda, and ECS/EKS).
Clean, organize, and validate datasets for analysis, and support core database tasks.
Monitor cloud system health using tools like AWS CloudWatch and evaluate real-time AI performance against business KPIs.
Create visualizations or reports to benchmark user satisfaction.
Present work during team demo days and participate in peer code reviews.