Job Description & Details
This is a solid enterprise engineering gig bridging traditional Java backend development with modern LLM tooling. If you like building scalable distributed systems but want to move past standard CRUD apps and into Retrieval-Augmented Generation (RAG) and document summarization, this role gives you a great sandbox to do that.
What You'll Actually Be Doing
You'll spend your days cutting production-grade code in Java using Spring Boot, spinning up distributed REST APIs, and figuring out how to reliably hook LLM pipelines into legacy enterprise architectures. The real challenge here isn't just writing the microservices, but making sure your AI document summarization tools and prompt-based workflows don't bottleneck the system when concurrency spikes.
The Core Tech Stack
You've got to live and breathe modern Java, Spring Boot, and robust microservices architecture. Beyond standard backend chops, you need tangible, hands-on reps with Large Language Models, RAG patterns, and specifically tools like Claude AI. They aren't looking for someone who just reads about AI on LinkedIn; they want a systems thinker who knows how to blend NLP fundamentals with cloud infrastructure on AWS, Azure, or GCP.
Interview Expectations
Expect the engineering panel to ask you how you would design a fault-tolerant microservice that consumes a slow, external LLM API without taking down the entire transaction thread through the API gateway. The hiring manager is secretly checking if you understand asynchronous programming and circuit breakers, or if you're just going to block threads waiting for an AI model to finish generating a response. They'll also likely grill you on how you handle context window limits and chunking strategies when implementing RAG for massive enterprise documents.
Application Advice
Skip the generic tech buzzwords and make sure your resume puts your Java microservices architecture front and center right next to your AI integration projects. You need to explicitly highlight keywords like Spring Boot, REST APIs, Claude AI, RAG, and document summarization to easily clear their automated ATS filters. If you've previously built caching layers or rate limiters for expensive LLM calls, feature that prominently as it directly addresses the hidden scalability pain points of this role.