Job Description & Details
This is a forward-looking backend engineering role that bridges traditional enterprise Java development with modern generative AI integrations. If you are tired of building standard CRUD apps and want to actually wire up LLMs and intelligent automation into production systems, this is worth looking at.
What You'll Actually Be Doing
You will spend your days architecting robust microservices in Spring Boot while figuring out how to inject AI capabilities like semantic search, content generation, and automated workflows into the core backend. Expect to collaborate heavily with data scientists and product managers to take experimental LLM features and turn them into scalable, low-latency, production-ready APIs.
The Core Tech Stack
Your bread and butter here will be Java and Spring Boot for building distributed microservices, but the real differentiator for this team is your ability to interface with AI APIs and Large Language Models. You need to be deeply comfortable designing RESTful endpoints that handle asynchronous AI workloads without choking your database or blowing up your latency.
Interview Expectations
Expect the hiring team to test both your classic backend chops and your practical AI integration knowledge. They will likely ask you how you handle rate limiting, token usage, and fallback mechanisms when an external LLM API goes down or latency spikes. They want to see that you can build resilient systems around non-deterministic AI outputs rather than just writing basic prototype scripts.
Application Advice
To get past the ATS and grab the recruiter's attention, make sure your resume clearly highlights production deployments using Spring Boot alongside any real-world experience connecting applications to LLMs, vector databases, or AI APIs. Don't just list Java; show how you scaled microservices and handled complex integrations under load.