Lead Infrastructure Engineer

This range is provided by Ragie. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$200,000.00 / yr - $250,000.00 / yr

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Fractional Head of Technical Recruitment | Podcast Host | YouTuber @yournexttechstartup 🎧

Ragie is a fully managed RAG-as-a-service platform that makes building RAG applications seamless for AI developers. We streamline ingestion, chunking, and multimodal indexing of structured and unstructured data, with built-in features like query rewriting, reranking, and flexible vector filtering to help enterprises deliver state-of-the-art GenAI. Ragie integrates directly into enterprise workflows, injecting accurate and actionable knowledge into mission-critical systems.

We are growing and we are looking for a Senior Infrastructure Engineer to join our core engineering team. This role is a unique opportunity to design and own the infrastructure behind a platform that runs multimodal GenAI workloads at scale. You’ll work with cutting-edge infrastructure technologies across multi-cloud environments , GPU-accelerated compute , and large-scale AI / LLM deployments .

Responsibilities

• Architect, build, and maintain the foundational infrastructure for Ragie’s AI platform.
• Design and manage multi-cloud deployments across providers such as Google Cloud, AWS, and Azure.
• Deploy and scale GPU clusters for running LLMs and multimodal workloads efficiently.
• Lead the setup and configuration of Kubernetes clusters for container orchestration and workload management.
• Implement Docker-based containerization strategies to streamline application deployment and scaling.
• Deploy and manage Elasticsearch clusters for large-scale indexing and retrieval.
• Integrate and optimize vector databases (e.g., Pinecone, Weaviate, or equivalent) for hybrid search workloads.
• Design and manage SQL and NoSQL databases, ensuring performance, reliability, and scalability.
• Establish robust observability practices with monitoring, logging, and metrics solutions.
• Partner closely with product and ML engineers to translate requirements into scalable, cost-efficient infrastructure.
• Continuously optimize infrastructure for performance, resilience, and efficiency.

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