Sobre esta vaga de Data Engineer - AI, Agents, & Context - Clinical (Sr. Associate) na Huron
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 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 innovation
Deliver transformational speed and leverage — faster time-to-insight, higher automation of knowledge work, and a foundation that scales AI safely and reliably as adoption grows
Unlock new capabilities across our business and create the foundation that drives deeper domain innovation and cross-domain collaboration
This 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 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 semantic and governed data products
Implement 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 warehouse/lakehouse 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 enterprise knowledge in coordination with Security/Legal/Compliance
Travel Expectations
Ability to travel as needed up to 4 times per year.
Required Qualifications
BA or BS required, preferably in Computer Science, Engineering, or a technology-based discipline
3–6 years in data engineering or data platform roles with strong hands-on delivery
Strong SQL and Python (or Scala/Java); solid production engineering habits
Experience designing and operating cloud data pipelines at scale
Experience working with unstructured data processing and search/retrieval concepts
Clear communicator who can work effectively across technical and functional teams
Preferred Qualifications
Hands-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 layers
Experience in regulated environments and data governance programs
Example Success Measures
Measurable improvement in AI outcomes: higher retrieval precision/recall, better citation coverage, fewer "missing context" failures
Reduced latency/cost per retrieval and improved platform reliability (SLO attainment, lower MTTR)
Consistent application of semantic definitions and context contracts across assigned workstreams
Delivery quality: production-ready outputs with minimal rework, well-documented and maintainable
Behavioral Attributes
Eager to learn the domain: Proactively builds familiarity with healthcare processes, terminology, and KPIs — can engage credibly with SMEs and ask the right clarifying questions
Collaborative and stakeholder-aware: Works well with engineers, consultants, and functional partners; communicates progress and flags risks clearly
Consultative problem-solver: Approaches requests with a "diagnose before prescribe" mindset — proposes options and works toward durable solutions rather than one-off fixes
High 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 quality
Clear communicator: Can go deep with engineers and explain concepts plainly to non-technical partners; writes solid docs and runbooks
Pragmatic builder: Biases toward shipping value in iterations, validating with users, and improving based on feedback
Comfortable with ambiguity: Adapts quickly in evolving AI/data product environments and turns unclear goals into actionable tasks
Integrity and stewardship: Handles sensitive data responsibly and respects established governance patterns
The 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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