Note: The job is a remote job and is open to candidates in USA. DigitalOcean is a leading cloud infrastructure provider, and they are seeking a Principal Engineer to define the technical direction and architecture for AI Data Infrastructure. The role involves leading the design, development, and operation of services for AI-native applications, ensuring they are reliable, scalable, and efficient.
Responsibilities
- 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 DigitalOcean 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
- 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
- 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 operational dashboards for latency, throughput, recall quality, freshness, availability, error rates, cost, and customer-visible reliability
- Drive automation for provisioning, upgrades, scaling, monitoring, alerting, incident response, and fleet operations
- Build systems that scale from small developer workloads to large production AI applications with billions of objects, high-dimensional vectors, high query volume, and strict latency expectations
- Set the technical vision for AI Data Infrastructure and influence architecture across multiple teams
- Lead design reviews and author technical proposals that clarify trade-offs, risks, sequencing, and long-term platform implications
- Establish standards for service design, APIs, data modeling, observability, operational readiness, testing, and production excellence
- Mentor senior and staff engineers, helping them make better architectural decisions and operate with higher technical judgment
- Create a culture where engineers understand not only how a system works, but why the design is correct for the customer and business
- Work with product, engineering, design, sales engineering, support, and go-to-market teams to understand customer problems and convert them into scalable platform capabilities
- Partner with customer-facing teams on architecture patterns for AI-native applications, retrieval-augmented generation, agentic workflows, and enterprise knowledge systems
- Translate complex technical concepts into clear guidance for executives, product leaders, engineering teams, and customers
- Help define migration and adoption paths for customers moving from self-managed vector databases, custom RAG pipelines, fragmented knowledge stores, or prototype agent memory systems to DigitalOcean-managed services
- Research and evaluate emerging technologies in vector databases, graph databases, AI memory, context engineering, retrieval evaluation, multimodal indexing, and agent data infrastructure
- Identify which capabilities DigitalOcean should build, partner for, or integrate from open source
- Build durable platform primitives rather than one-off features, ensuring DigitalOcean’s AI data services remain simple, composable, open, and cost-effective
- Drive the evolution from basic retrieval infrastructure toward intelligent data systems that help agents learn, remember, and improve over time
Skills
- 12+ years of experience designing and building distributed systems, databases, storage systems, search infrastructure, data platforms, or cloud infrastructure at scale
- Deep technical expertise in vector databases, search systems, database internals, or distributed data infrastructure
- Strong understanding of vector indexing, ANN search, hybrid search, semantic search, metadata filtering, reranking, query planning, storage engines, caching, replication, and high availability
- Experience designing or operating production-grade services for AI, data, search, analytics, databases, or retrieval-heavy workloads
- Familiarity with knowledge base systems, retrieval-augmented generation, embedding pipelines, chunking strategies, context windows, memory systems, and agentic AI application patterns
- Strong systems architecture judgment, including the ability to reason through consistency, latency, availability, durability, cost, scale, and operational trade-offs
- Hands-on experience with cloud-native infrastructure, Kubernetes, observability systems, infrastructure as code, CI/CD, and production operations
- Fluency in one or more backend systems languages such as Go, Java, C++, Rust, or Python
- Proven ability to lead large, ambiguous, cross-team technical initiatives without relying on formal authority
- Strong written and verbal communication skills, with the ability to explain complex architecture clearly to both technical and business audiences
- A track record of mentoring engineers and raising the technical bar across an organization
- Experience with graph databases, knowledge graphs, context graphs, or graph-based retrieval is strongly preferred
Benefits
- Reimbursement for relevant conferences, training, and education
- All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development
- Employee Assistance Program
- Local Employee Meetups
- Flexible time off policy
- Bonus in addition to base salary; bonus amounts are determined based on company and individual performance
- Equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program
Company Overview
Company H1B Sponsorship