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Role SummaryThis role sits within a strategic investment to embed AI into how we operate, serve customers, and make decisions within our healthcare business. We're building a healthcare-wide AI data and context platform with a focus on deep domain expertise embedded throughout our architecture. Our goals are:Turn structured and unstructured information into trusted, reusable "building blocks" (semantic layers, retrieval services, and agent-ready interfaces) that accelerate product innovationDeliver transformational speed and leverage — faster time-to-insight, higher automation of knowledge work, and a foundation that scales AI safely and reliably as adoption growsUnlock new capabilities across our business and create the foundation that drives deeper domain innovation and cross-domain collaborationThis is a hands-on technical contributor who builds and maintains core AI/context data capabilities. The role executes key parts of the 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 ResponsibilitiesBuild and contribute to the AI context platformImplement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/servingBuild and maintain patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sourcesContribute to retrieval quality improvements (query strategies, hybrid search, metadata filtering) in partnership with AI engineersDeliver semantic and governed data productsImplement semantic layers (metrics/entities) that power BI and agent reasoning consistentlyApply established data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations)Ensure datasets and indexes are documented and reusableOperational excellenceSupport reliability and performance across assigned workstreams: monitoring, alerting, runbooks, and incident responseContribute to cost and latency optimization across warehouse/lakehouse and vector infrastructureAI safety and complianceApply security-by-design patterns: RBAC/ABAC, PII redaction, retention controls, and audit loggingFollow established guardrails for AI access to enterprise knowledge in coordination with Security/Legal/ComplianceTravel ExpectationsAbility to travel as needed up to 4 times per year.Required QualificationsBA or BS required, preferably in Computer Science, Engineering, or a technology-based discipline3–6 years in data engineering or data platform roles with strong hands-on deliveryStrong SQL and Python (or Scala/Java); solid production engineering habitsExperience designing and operating cloud data pipelines at scaleExperience working with unstructured data processing and search/retrieval conceptsClear communicator who can work effectively across technical and functional teamsPreferred QualificationsHands-on experience with vector search and embeddings (pgvector/Pinecone/Weaviate/OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking)Experience supporting LLM applications (RAG, agent tool interfaces, evaluation/observability)Familiarity with knowledge graphs/semantic modeling or metrics layersExperience in regulated environments and data governance programsExample Success MeasuresMeasurable improvement in AI outcomes: higher retrieval precision/recall, better citation coverage, fewer "missing context" failuresReduced latency/cost per retrieval and improved platform reliability (SLO attainment, lower MTTR)Consistent application of semantic definitions and context contracts across assigned workstreamsDelivery quality: production-ready outputs with minimal rework, well-documented and maintainableBehavioral AttributesEager to learn the domain: Proactively builds familiarity with healthcare processes, terminology, and KPIs — can engage credibly with SMEs and ask the right clarifying questionsCollaborative and stakeholder-aware: Works well with engineers, consultants, and functional partners; communicates progress and flags risks clearlyConsultative problem-solver: Approaches requests with a "diagnose before prescribe" mindset — proposes options and works toward durable solutions rather than one-off fixesHigh ownership and follow-through: Treats reliability, documentation, and operational readiness as part of the work; finishes what they start; holds a high bar for production qualityClear communicator: Can go deep with engineers and explain concepts plainly to non-technical partners; writes solid docs and runbooksPragmatic builder: Biases toward shipping value in iterations, validating with users, and improving based on feedbackComfortable with ambiguity: Adapts quickly in evolving AI/data product environments and turns unclear goals into actionable tasksIntegrity and stewardship: Handles sensitive data responsibly and respects established governance patternsThe estimated base salary for this job is $110,000 - $150,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $123,200 - $177,000 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.
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