AI Systems Engineer - Data & State Management - Senior

Location:

Anywhere in Country
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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.

• 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 Jet Stream (agent-to-agent), Debezium (change data capture), Apache Flink (stream processing), and Apicurio/Cloud Events (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.

• 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.

• 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 patterns — embeddings, retrieval, feature/state stores for agentic workloads.
• Production experience with vector and/or graph databases (Qdrant, Milvus, PGVector, Neo4j) in AI/ML contexts.
• Deep experience with event streaming and…