Everpure
Fullstack Software Engineer, AI Infra
About this role
Build enterprise-grade RAG infrastructure as a fullstack engineer on Everpure's AI data platform. You'll develop scalable backend and frontend systems in Go and Python, collaborate with NVIDIA on cutting-edge AI integration, and own features from concept through production deployment.
What you'll do
- Design and optimize full-stack RAG infrastructure using Go and Python for ultra-low latency enterprise data retrieval
- Integrate state-of-the-art AI tooling and model acceleration libraries in partnership with NVIDIA engineering teams
- Lead end-to-end feature development from requirements through frontend UI and backend microservices to production
- Establish technical standards, automated testing pipelines, and performance benchmarks for system reliability
- Solve complex architectural trade-offs and drive innovative solutions with cross-functional collaboration
- Maintain enterprise-grade security and high availability across the entire platform
What they're looking for
- Go programming
- Python programming
- Backend microservices architecture
- Frontend application development
- Retrieval-Augmented Generation (RAG)
- LLM pipelines and GenAI systems
- System design and scalability
- Automated testing and performance optimization
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Everpure
Everpure builds cloud-native storage and data infrastructure solutions, including Kubernetes platforms and enterprise SSD products for mission-critical environments. The company is hiring technical support engineers, validation engineers, and solutions specialists to diagnose complex infrastructure issues, qualify storage hardware, and deliver customer support across cloud and storage technologies.
- Website
- everpure.com
Likely interview questions
- Describe your experience building and deploying RAG or LLM-based systems in production environments.
- Walk us through an end-to-end feature you owned—from design through deployment—and how you handled trade-offs.