AI & DevOps Engineering Intern

External

Revvity · Waltham

Part-timeOn-siteToday

AWSCI/CDDockerGenerative AIJavaScriptMachine Learning

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About the role

Revvity is a developer and provider of end-to-end solutions designed to help scientists, researchers, and clinicians solve the world's greatest health challenges. We pair the enthusiasm of an industry disruptor with the experience of a longtime leader. Our team of 11,000+ colleagues from around the globe are vital to our success and the reason we're able to push boundaries in pursuit of better human health. Find your future at Revvity Are you passionate about artificial intelligence and eager to see how generative AI and Machine Learning (ML) can transform real-world business operations? We are seeking a dynamic, self-motivated Enterprise AI & DevOps Engineering Intern to join our team. This program offers a unique opportunity to gain hands-on experience researching, building, and scaling AI/ML solutions. As our primary interns, you won't just be sitting on the sidelines-you will learn, develop, and completely own end-to-end applications and deployment pipelines. Sitting at the intersection of full-stack development, cloud infrastructure, and business strategy, you will work closely with experienced mentors and cross-functional teams to drive innovative projects from inception to production. If you are a self-starter who loves to learn, hack on modern tech stacks, and take full ownership of what you build, this is the perfect playground for you.

Responsibilities

Within 30 Days (Operational Discovery & Deep-Dive):

Operational Deep-Dive: Complete your technical and corporate onboarding, shifting quickly into an exploratory phase to map our enterprise systems, business architecture, and active AI workstreams.

Cross-Functional Discovery: Conduct informational sessions across business units to shadow team members, document operational friction points, and identify high-value opportunities for AI/ML intervention.

Tech & Trend Research and Learning: Dive deep into researching emerging AI/ML technologies, industry trends, and software testing methods, actively learning our internal software development best practices, coding standards, and core development tools.

Value Mapping & Feedback: Synthesize your findings to highlight where generative AI can reduce manual overhead or unlock creative capacity, while providing proactive feedback on optimizing the onboarding experience.

Beyond 30 Days (Own, Build, and Scale):

Rapid Prototyping & Full-Stack Sandbox: 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.

Context Engineering & Model Optimization: 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.

End-to-End Pipeline Ownership: Take full ownership of the deployment pipeline. You will design, implement, and maintain robust CI/CD pipelines-automating LLM application deployments and writing automated testing workflows from scratch.

Containerization & Cloud Infrastructure: Get under the hood of modern infrastructure by spearheading containerization efforts with Docker, learning to orchestrate, configure, and manage cloud-based deployments on AWS (EC2, S3, Lambda, and ECS/EKS).

Data Engineering & Analytics: Help clean, organize, and validate datasets for analysis. Support core database tasks like running queries and managing data entry to fuel your deployed ML models.

Observability & Performance Tracking: 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.

Tech Showcases & Collaboration: Have fun presenting your hard work! Participate in peer code reviews to learn from senior engineers and showcase your fully operational MVPs during team demo days-translating complex architecture and AI concepts into clear, engaging insights for stakeholders.

AI & DevOps Engineering Intern

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