Key Responsibilities 

Architect and own the AI context platform 

• Design end-to-end platform architecture: ingestion → parsing/chunking → enrichment → embeddings → vector indexing → retrieval/serving 

• Define scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources 

• Set technical direction for retrieval quality (query strategies, hybrid search, metadata filtering, reranking) in partnership with AI engineers 

• Evaluate and select infrastructure, tooling, and cloud services to support platform needs across AWS/Azure/GCP environments 

Design and deliver semantic and governed data products 

• Architect and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently across the platform 

• Define data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations) 

• Establish standards for discoverability, documentation, and reusability across datasets and indexes 

• Own the dbt or semantic layer tooling strategy and ensure consistent application across workstreams 

Operational excellence 

• Own reliability and performance at the platform level: monitoring, alerting, SLAs/SLOs, runbooks, incident response, and postmortems 

• Drive cost and latency optimization across Snowflake, lakehouse, and vector infrastructure 

• Set engineering standards for CI/CD, testing, and evaluation (retrieval eval sets, regression tests, online telemetry) 

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 to define and enforce guardrails for AI access to enterprise knowledge 

• Own governance patterns for sensitive data handling across the platform 

Lead through influence 

• Drive technical roadmap decomposition with product, AI, and application stakeholders 

• Facilitate architectural decisions across teams and functions, building alignment without direct authority 

• Set best practices and mentor engineers via design reviews, code reviews, and documentation 

Future Scope 

This role is expected to grow into direct people leadership over time. As the platform matures and the engineering team expands, the Architect will take on formal responsibility for leading a small team of engineers — owning hiring input, technical development, and delivery oversight. Candidates should be comfortable with that trajectory and motivated by the opportunity to build and shape a team from an early stage. 
 

Travel Expectations

• Ability to travel as needed up to 4 times per year.

Required Qualifications 

• Bachelor's Degree in computer science, engineering, or related field of study

• 8–12+ years in data engineering, data architecture, or platform roles with significant hands-on delivery 

• Expert SQL and strong Python (or Scala/Java); deep production engineering habits 

• Hands-on Snowflake expertise including advanced data modeling, pipeline design, performance tuning, and operating at scale in production 

• Proven experience designing cloud data architectures on AWS, Azure, or GCP — including storage, compute, orchestration, and networking considerations 

• Hands-on experience with vector search and embeddings (pgvector/Pinecone/Weaviate/OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking) 

• Experience with dbt or comparable semantic layer tooling in a production environment 

• Demonstrated ability to lead cross-functional technical initiatives and drive alignment across teams 

• Strong written and verbal communication skills — able to present architecture decisions to both technical and non-technical audiences 

Preferred Qualifications 

• 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 data governance programs 

• Familiarity with Iceberg, Delta Lake, or other open table formats in a lakehouse context 

• Prior experience in a formal or informal technical lead or staff engineer capacity 

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, context contracts, and platform standards across teams 

• Architecture decisions are well-documented, defensible, and enable downstream engineers to deliver faster 

• Platform design earns trust from Security, Compliance, and business stakeholders 

Behavioral Attributes 

• Business-curious and domain-eager: Proactively learns healthcare processes, terminology, and KPIs — 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; 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 without relying on positional authority 

• 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 for mixed audiences: Can go deep with engineers and explain concepts plainly to non-technical partners; writes crisp architecture 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, adapts quickly, and turns unclear goals into actionable architectural plans 

• Integrity and stewardship: Handles sensitive data responsibly, advocates for secure-by-design patterns, and enables the business to move fast without cutting corners on governance 

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.

#LI-REMOTE

#LI-CL1