Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem.
This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.
THE ROLE
Our engineering team thrives on challenging the norm and drawing from diverse technical backgrounds to build some of the industry’s most reliable, intuitive systems. We are forming a dedicated AI Applications team focused on building systems, tools, and processes that elevate the developer experience and accelerate innovation.
We are seeking an experienced Senior Platform Engineer to lead the "Inner Loop" of AI development. In this role, you will bridge high-velocity AI experimentation and enterprise-grade production infrastructure ensuring LLM applications, RAG pipelines, and agentic workflows are built, tested, and deployed with high reliability and efficiency.
WHAT YOU’LL DO
Deliver Platform Self-Service: Design internal APIs and abstractions allowing engineers to provision AI-ready environments complete with model weights, vector databases, and event streams via single-command automation.
Build Agentic & Go-Based Automation: Develop robust, type-safe internal tooling and AI agents in Go and Python to automate root-cause analysis, disaster recovery, and proactive infrastructure optimization.
Scale Event-Driven AI Pipelines: Architect asynchronous processing systems using Kafka and RabbitMQ to trigger dynamic infrastructure scaling and handle high-volume data streams like long-running document ingestion.
Ensure Reproducible Deployments: Maintain "Golden Path" deployment standards using Docker and Kubernetes to keep models, prompts, and application code perfectly synchronized across all environments.
Optimize AI Observability & Performance: Manage GPU workloads, distributed caches (Redis), and vector databases while monitoring system health and AI-specific metrics like token latency and model drift.
WHAT YOU BRING
Relevant Infrastructure Experience: 8+ years of experience in Platform Infrastructure Engineering with an emphasis on supporting AI application development and platform self-service.
Strong Programming Expertise: High proficiency in Golang.
Practical AI & Storage Knowledge: Hands-on experience integrating LLM APIs, prompt engineering, and RAG pipelines, alongside expertise in managing vector databases and distributed caches.
Orchestration & Event-Driven Systems:
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