Note: The job is a remote job and is open to candidates in USA. EY is a globally connected professional services organization focused on building a better working world through data, AI, and advanced technology. EY is seeking a Senior AI Systems Engineer to own the stateful backbone of its hybrid AI platform, including production data stores, memory tiers, event streaming, change-data-capture pipelines, durability, consistency, and recoverability across cloud, on-premises, edge, client-managed, and air-gapped environments.
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
- • 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 messaging (Apache Kafka/Strimzi, NATS) and change data capture (Debezium)
- • Experience with stream processing (Apache Flink) and event/schema governance (CloudEvents, schema registry)
- • Experience running stateful systems on Kubernetes (operators, persistent volumes, object/block storage such as MinIO/OpenEBS)
- • Ability to define clean ownership boundaries and data/schema contracts with platform, trust, runtime, and delivery teams
- • Experience with durable workflow engines (DBOS, Temporal, or equivalents)
- • Experience with data lineage and metadata tooling (OpenLineage, Marquez, or equivalents)
- • Proven track record operating data systems under compliance, security, or regulatory constraints, including data-at-rest and in-transit protection
- • Familiarity with multi-tenant data isolation and per-tenant performance management
- • Experience with cross-environment replication and DR strategies tiered by RPO/RTO (e.g., MirrorMaker, CloudNativePG PITR)
- • Exposure to regulated delivery environments (financial services, tax, healthcare, risk)
Benefits
- Medical and dental coverage
- Pension and 401(k) plans
- A wide range of paid time off options
- Team-led and leader-enabled hybrid model
- Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances.
- Time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.
Company Overview
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