AI Systems Engineer - Data & State Management - Senior

Location: Anywhere in Country At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. The opportunity We are seeking an AI Systems Engineer to own the stateful backbone of EY’s AI-native platform, including the data stores, memory tiers, and event streaming systems that hold and move every piece of state in EY’s Hybrid AI Multi-Environment Runtime (HAI). This role ensures that agentic AI workloads have durable, performant, and consistent access to data across cloud, on-prem, edge, client-managed, and air-gapped environments. Within HAI, this is a distinct discipline from platform operations and model serving. This role owns everything that must remember, persist, or flow: relational and key-value state, vector and graph stores, object storage, durable workflows, and the streaming and change-data-capture pipelines that connect them. It is the layer that makes the platform stateful, reliable, and event-driven. It is ideal for a data-infrastructure engineer who is equally comfortable operating production databases and high-throughput streaming systems, who treats data durability, consistency, and recoverability as non-negotiable in regulated client contexts, and who understands that state is the hardest part of any distributed platform to get right. Your Key Responsibilities • Own the memory and data stores: relational and durable state (PostgreSQL, DBOS durable workflows), caching (Redis/Valkey), vector stores (Qdrant/Milvus/PGVector), knowledge graphs (Neo4j), and object/block storage (MinIO, OpenEBS Mayastor), across every environment and tenant. • Own event streaming and async messaging: Apache Kafka (Strimzi), NATS JetStream (agent-to-agent), Debezium (change data capture), Apache Flink (stream processing), and Apicurio/CloudEvents (schema and event contracts). • Own data durability, consistency, and recoverability: replication, backup/restore, point-in-time recovery, and cross-environment data movement, tiered by RPO/RTO. • Build and operate streaming and CDC pipelines that move data reliably between stores and services, with schema governance and evolution that prevents breaking changes across producers and consumers. • Make state multi-tenant and portable, ensuring isolation, performance, and consistent semantics whether running on managed cloud services or self-hosted OSS in an air-gapped environment. • Provide the data and lineage substrate that downstream governance, observability, and AI knowledge capabilities depend on, as well as integrating with lineage tooling. Skills And Attributes For Success • Deep expertise operating production databases and data stores at scale, including relational, key-value, vector, graph, and object storage. • Strong command of streaming and event-driven architectures (Kafka, NATS, CDC, stream processing) and the consistency tradeoffs they involve. • A durability-first mindset: thinking in terms of consistency, recoverability, blast radius, and data correctness under failure. • Ability to operate stateful systems consistently across managed cloud and self-hosted OSS in cloud, on-prem, edge, and air-gapped environments. • Strong grasp of schema governance and evolution, preventing breaking changes across producers and consumers. • Strong communicator able to guide consuming teams toward the right storage and streaming patterns. • Orientation toward reliability and toil reduction through automation and infrastructure-as-code for data systems. To qualify you must have • Bachelor’s or Master’s degree in Computer Science or related technical field. • 8+ years operating production data infrastructure, streaming systems, or database platforms at scale. • Hands-on expertise with relational databases (PostgreSQL) and caching (Redis/Valkey), including HA, replication, and backup/recovery. • Exposure to AI/ML data…