AI & DevOps Engineering Intern

Revvity•Waltham, MA

1d•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. The intern will learn, develop, and completely 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 role for a self-starter who loves 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 is a plus.
Professional cloud computing certifications or training (even if currently in progress) are highly regarded.

Responsibilities

Complete technical and corporate onboarding, shifting quickly into an exploratory phase to 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 high-value opportunities for AI/ML intervention.
Dive deep into researching emerging AI/ML technologies, industry trends, and software testing methods, actively 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, while providing proactive feedback on optimizing the onboarding experience.
Write, debug, and develop full-stack proof-of-concepts (including interactive user interfaces and web pages), leveraging Python backends and JavaScript frontends to build highly responsive, AI-powered tools.
Formulate advanced context engineering strategies—optimizing how enterprise data, memory systems, tool outputs, and prompt chaining are orchestrated within the LLM's context window to drive accurate, structured outputs.
Take full ownership of the deployment pipeline, designing, implementing, and maintaining robust CI/CD pipelines—automating LLM application deployments and writing automated testing workflows from scratch.
Spearhead containerization efforts with Docker, learning to orchestrate, configure, and manage cloud-based deployments on AWS (EC2, S3, Lambda, and ECS/EKS).
Help clean, organize, and validate datasets for analysis. Support core database tasks like running queries and managing data entry to fuel deployed ML models.
Monitor cloud system health using tools like AWS CloudWatch, evaluate real-time AI performance against business KPIs, and create visualizations or reports to benchmark user satisfaction.
Present work, participate in peer code reviews to learn from senior engineers, and showcase fully operational MVPs during team demo days—translating complex architecture and AI concepts into clear, engaging insights for stakeholders.