Principal Engineer, AI Data Infrastructure

Dive in and do the best work of your career at Nimbus Data Systems. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. We are looking for a Principal Engineer to define the technical direction and architecture for AI Data Infrastructure at Nimbus Data Systems. This role will lead the design, development, and operation of services that help AI-native applications ground, retrieve, reason over, and remember data at scale. These services will power Nimbus Data Systems’s Agentic AI and Inference customers by providing production-grade knowledge bases, vector search, hybrid retrieval, context management, memory systems, and graph-based data infrastructure. As a Principal Engineer, you will work across engineering, product, platform, and customer-facing teams to build foundational AI data services that are reliable, performant, scalable, cost-efficient, and simple for developers to use. You should be equally comfortable setting long-term architecture, making hard technical trade-offs, mentoring senior engineers, and going deep into system design when the business depends on getting the architecture right. We are looking for someone who can span technical strategy and hands-on execution—someone who has strong distributed systems judgment, understands database and retrieval system internals, and can turn emerging AI infrastructure patterns into durable cloud services. What You’ll Do Architect and guide the implementation of high-scale, reliable, secure AI data infrastructure services for agentic and inference workloads. Define the technical architecture for vector databases, knowledge bases, hybrid search, semantic search, context graphs, agent memory, and retrieval orchestration. Make foundational decisions on indexing, storage layout, sharding, replication, caching, query execution, ranking, consistency, latency, availability, and cost-performance trade-offs. Design systems that support multiple retrieval patterns, including dense vector search, keyword/BM25 search, metadata filtering, reranking, graph traversal, and context-aware retrieval. Build and operate managed services that customers can trust for production AI workloads, including observability, SLOs, capacity planning, backups, upgrades, failover, and disaster recovery. Partner with product managers and engineering leaders to translate customer needs and business priorities into a clear multi-year technical roadmap. Collaborate with Inference, Managed Databases, Storage, Kubernetes, App Platform, IAM, and Observability teams to ensure AI data services are deeply integrated into the Nimbus Data Systems platform. Identify architectural bottlenecks, scaling risks, retrieval quality gaps, operational weaknesses, and cost inefficiencies before they become customer-impacting problems. Establish engineering standards, design review practices, operational mechanisms, and technical decision frameworks for AI data infrastructure. Mentor engineers across teams and raise the bar for architectural rigor, operational excellence, systems thinking, and customer impact. Stay current with advances in vector databases, retrieval-augmented generation, graph databases, memory systems, embedding models, reranking, agent frameworks, and AI data management. Key Responsibilities Architect and Build Design and evolve distributed AI data systems optimized for low latency, high recall, high availability, strong operational control, and efficient unit economics. Lead architecture for vector indexing and retrieval systems, including ANN algorithms, HNSW-style indexes, quantization, compression, partitioning, filtering, and recall-latency trade-offs. Architect knowledge base infrastructure, including ingestion, chunking, embedding generation, indexing, metadata management, retrieval, reranking, evaluation, and re-indexing workflows. Design context management and memory systems that enable agents to persist, retrieve, summarize, and reason over relevant state across sessions and tasks. Evaluate when to use vector search, lexical search, relational stores, object storage, graph databases, or purpose-built retrieval layers—and design clean integration patterns across them. Take a hands-on technical leadership role when needed to unblock delivery, validate architecture, or guide implementation of critical systems. Reliability, Performance, and Scale Own architectural mechanisms for availability, failover, durability, capacity management, tenant isolation, cost controls, and operational safety. Lead performance tuning across ingestion, embedding, indexing, query serving, graph traversal, reranking, and retrieval pipelines. Define SLOs and operati