Job Description & Details
This is a solid backend and generative AI role for someone tired of building endless Jupyter notebook prototypes that never see the light of day. You will be building and shipping production-grade LLM applications that actually impact real customers out in the wild.
What You'll Actually Be Doing
You'll spend the vast majority of your time writing code, splitting your efforts between GenAI development and core backend service engineering. Your day-to-day involves designing resilient APIs, wiring up LLMs, and making sure your AI services don't fall over under production traffic. You won't just build the feature and walk away; you'll own the monitoring, evaluation pipelines, and architectural decisions from concept to launch.
The Core Tech Stack
You need a rock-solid foundation in backend programming using Python, Java, or Go, paired with hands-on experience deploying machine learning or LLM workflows into production environments. Cloud infrastructure and distributed systems knowledge are non-negotiable here because you'll be dealing with scaling issues and high-availability service design. Knowing your way around LLM evaluation methodologies will set you apart from standard backend devs.
Interview Expectations
Expect the hiring team to grill you on how you handle failure modes when an external LLM API goes down or latency spikes in production. They want to hear about your strategies for fallback mechanisms, caching, and prompt engineering reliability. You should also be ready for a deep-dive system design question where you build a scalable RAG pipeline or high-throughput API from scratch, focusing heavily on bottlenecks and observability.
Application Advice
Skip the generic AI buzzwords and focus heavily on your actual production backend experience. Make sure your resume explicitly highlights how you've scaled services, managed cloud infrastructure, and pushed machine learning models into live environments rather than just local test rigs. Weave in keywords like distributed applications, API development, LLM evaluation, and production monitoring to easily clear the ATS filters.