Job Description

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 an 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. Create the foundation that drives deeper domain innovation and allows cross-domain collaboration to flourish.

This is a hands-on technical leader who builds core AI/context data capabilities and leads delivery through architecture, implementation, and mentorship. The role owns key parts of the AI context platform—unstructured ingestion, embeddings, retrieval, semantic layers, and governance—while partnering across teams to ship production-grade AI data products.

This roles does not have direct reports initially. Leadership is through technical ownership and influence.

Key responsibilities (hands-on + technical leadership)

Build and own the AI context platform

• Design and implement end-to-end pipelines: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving.
• Build scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources.
• Improve retrieval quality (query strategies, hybrid search, metadata filtering, reranking hooks) in partnership with AI engineers.

Deliver semantic and governed data products

• Define and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently.
• Establish data contracts and “context contracts” for AI inputs (schemas, metadata requirements, freshness, citation expectations).
• Ensure datasets and indexes are discoverable, documented, and reusable.

Operational excellence

• Own reliability and performance: monitoring, alerting, SLAs/SLOs, runbooks, incident response, postmortems.
• Optimize cost and latency across warehouse/lakehouse and vector infrastructure.

AI safety, governance, and compliance

• Implement security-by-design: RBAC/ABAC patterns, PII redaction, retention controls, audit logging, and safe access pathways for agent tools.
• Partner with Security/Legal/Compliance on guardrails for AI access to enterprise knowledge.

Lead through influence

• Drive technical direction and roadmap decomposition with product/AI/application stakeholders.
• Set best practices for testing, CI/CD, and evaluation (retrieval eval sets, regression tests, online telemetry).
• Mentor engineers via pairing, code reviews, and lightweight enablement sessions.

Required qualifications

• 6–10+ years in data engineering/platform roles with significant hands-on delivery.
• Expert SQL and strong Python (or Scala/Java); strong production engineering habits.
• Proven experience designing cloud data pipelines and operating them reliably at scale.
• Experience working with unstructured data processing and search/retrieval concepts.
• Strong communication skills and ability to lead cross-functionally.

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).
• Knowledge of knowledge graphs/semantic modeling or metrics layers at scale.
• Experience in regulated environments and mature 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).
• Broad adoption of semantic definitions and context contracts across teams.
• Accelerated delivery by enabling others via standards, templates, and mentorship.

Behavioral attributes

• Business-curious and eager to learn Proactively learns the functional domain (processes, terminology, KPIs, constraints) and can speak credibly with SMEs and business leaders—not just translate requirements, but help shape the right questions and success measures.
• Stakeholder-first collaborator Builds strong relationships with stakeholders, SMEs, and consultants; clarifies goals, constraints, and tradeoffs early; communicates progress and risks clearly; and sets realistic expectations around timelines, scope, and quality.
• Consultative problem-solver: Approaches requests with a “diagnose before prescribe” mindset—asks smart questions, proposes options, and guides teams toward durable solutions rather than one-off fixes.
• Influence without authority Leads through expertise and trust—drives alignment, facilitates decisions, and unblocks teams across functions even without direct reports.
• High ownership and follow-through: Treats reliability, documentation, and operational readiness as part of the work; finishes what they start; and holds a high bar for production quality.
• Clear communicator for mixed audiences: Can go deep with engineers and also explain concepts plainly to non-technical partners; writes crisp docs, designs, and runbooks.
• Pragmatic builder mindset: Biases toward shipping value in iterations, validating with users, and improving based on feedback—balancing innovation with maintainability and risk.
• Comfortable with ambiguity: Thrives in early-stage or evolving spaces (AI/data products), adapts quickly, and turns unclear goals into actionable plans.
• Integrity and stewardship: Handles sensitive data responsibly, respects governance, and advocates for secure-by-design patterns while enabling the business to move fast.

The estimated base salary for this job is $140,000 - $190,000 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 $161,000 - $237,500 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.

Position Level

Manager

Country

United States of America