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Niche knowledge-graph and vector retrieval needs reduce applicant pool despite the common senior software engineer title.
Requires specialized graph, vector retrieval, MDM, and regulatory experience, limiting transferable candidate pool.
Explicit 6+ years plus mandatory graph, vector retrieval, streaming, AWS, and IaC make shortlisting highly strict.
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Own and develop the core context graph data layer, including schema design, entity/relationship management, and temporal memory records.
Build and maintain robust and scalable ingestion pipelines capturing multi-source events with features like idempotency, ordering, and replay support.
Implement and optimize hybrid retrieval and public/internal APIs ensuring strict query latency, caching, multi-tenant security, and performance engineering across graph and vector stores.
6+ years backend engineering in Python, Go, or Java/Kotlin with production-scale service experience.
Hands-on experience with graph databases (Neptune, Neo4j, ArangoDB) or equivalent highly connected data modeling.
Practical experience with search/vector retrieval systems (OpenSearch/Elasticsearch, pgvector, or vector DB) including index tuning and relevance debugging.
Experience with data pipelines (Kafka/Kinesis or equivalent) supporting exactly-once/idempotent processing and schema evolution; Solid AWS (EKS/Lambda, DynamoDB, S3, IAM) and infrastructure-as-code experience.
Experienced in building backend systems critical to real-time AI agent platforms with strong focus on data modeling and query performance at scale.
Skilled in both graph and vector data technologies, capable of delivering multi-tenant secure, observable, and performant APIs and services.
Demonstrated ownership of end-to-end data lifecycle and compliance features such as TTL policies, archival, right-to-be-forgotten deletion, and audit logging within regulated environments.