Data Engineer - AI, Agents, & Context - Revenue Cycle (Associate)

Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We help healthcare organizations build innovation capabilities and accelerate key growth initiatives, enabling organizations to own the future, instead of being disrupted by it. Together, we empower clients to create sustainable growth, optimize internal processes and deliver better consumer outcomes. Health systems, hospitals and medical clinics are under immense pressure to improve clinical outcomes and reduce the cost of providing patient care. Investing in new partnerships, clinical services and technology is not enough to create meaningful and substantive change. To succeed long-term, healthcare organizations must empower leaders, clinicians, employees, affiliates and communities to build cultures that foster innovation to achieve the best outcomes for patients. Joining the Huron team means you’ll help our clients evolve and adapt to the rapidly changing healthcare environment and optimize existing business operations, improve clinical outcomes, create a more consumer-centric healthcare experience, and drive physician, patient and employee engagement across the enterprise. Join our team as the expert you are now and create your future. This role sits within a strategic investment to embed AI into how we operate, serve customers, and make decisions within our heal [... source excerpt omitted ...] he AI context platform — unstructured ingestion, embeddings, retrieval, and semantic layers — working closely with senior engineers and cross-functional partners to ship reliable, production-grade AI data products. Key Responsibilities Build and contribute to the AI context platform Implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving Build and maintain patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources Contribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineers Deliver s [... source excerpt omitted ...] mplement semantic layers (metrics/entities) that power BI and agent reasoning consistently Apply established data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations) Ensure datasets and indexes are documented and reusable Operational excellence Support reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident response Contribute to cost and latency optimization across Snowflake and vector infrastructure AI safety and compliance Apply security-by-design patterns: RBAC/ABAC, PII redaction, retention controls, and audit logging Follow established guardrails for AI access to e