Data & AI Platform Architect

ProviderTrust Summary ProviderTrust was founded in 2010 with a mission to create safer healthcare for everyone through OIG and state Medicaid exclusion monitoring. Today, the organization has developed the industry’s most accurate dataset for ongoing exclusion monitoring and primary source verification, serving the nation’s top health systems, payers, and healthcare organizations. Our solutions monitor employees, vendors, provider networks, licenses, credentials, and more for OIG and state Medicaid exclusions, sanctions or disciplinary actions, license expirations, or suspensions. With a team of 100+ employees, our Nashville-based company has consistently been recognized as one of the Best Places to Work by Modern Healthcare, Inc. Magazine, and the Nashville Business Journal. To learn more, visit [providertrust.com] We are looking for a senior, hands-on engineer to design and build our company's data warehouse and data lake foundation, and to lead the implementation of large language model (LLM) capabilities across both internal tooling and customer-facing products. This is a greenfield, high-ownership

role: you will influence the architecture and platform and tooling, and establish the standards the rest of the organization will build on for years to come. The ideal candidate is equally comfortable architecting a scalable data pipeline and designing a secure, production-grade LLM integration — including retrieval-augmented generation (RAG), prompt/response governance, and model evaluation.

Key Responsibilities Data Warehouse & Data Lake Architecture Design, build, and own the enterprise data warehouse and data lake (or lakehouse) architecture from the ground up, including ingestion, storage, transformation, and serving layers. Evaluate and select the core platform and supporting orchestration/ETL tooling based on cost, scale, and team skillset. Build reliable, well-documented pipelines that consolidate data from operational systems, third-party sources, and application logs into a single source of truth. Establish data modeling standards (e.g., dimensional modeling, medallion architecture) and enforce data quality, lineage, and observability practices. Implement role-based access controls, encryption, and data retention policies appropriate for sensitive and regulated data. Internal & External AI Platform Architect and implement internal LLM tooling (e.g., knowledge-base search, analyst copilots, internal chat assistants) that draws on the data warehouse/lake as a grounded knowledge source. Design and ship customer-facing (external) LLM-powered features, ensuring reliability, latency, and cost are production-ready at scale. Build retrieval-augmented generation (RAG) pipelines, including chunking, embedding, and vector search, to ground model outputs in company and customer data. Stand up evaluation, monitoring, and guardrail frameworks to catch hallucination, data leakage, and quality regressions before and after release. Own vendor and model strategy across proprietary APIs (OpenAI, Anthropic, Azure OpenAI, Bedrock) and, where appropriate, self-hosted or fine-tuned open-source models. Partner with security and compliance stakeholders to ensure LLM features meet data privacy, auditability, and regulatory

requirements (e.g., PII handling). Governance, Security & Cross-Functional Leadership Define and document architecture decisions, data governance policies, and AI usage guidelines for the broader engineering organization. Act as the internal subject-matter expert on data platform and applied AI/LLM strategy, advising product, engineering, and leadership on feasibility and roadmap. Mentor engineers as the team grows, and help recruit and evaluate future data/AI hires. Manage costs and performance across cloud data and AI infrastructure through capacity planning and optimization.

Qualifications Required 7+ years of experience in data engineering, with at least 3 years designing or owning a production data warehouse or data lake architecture from the ground up. Hands-on experience building and shipping LLM-powered features in production (RAG, agentic workflows, embeddings/vector search, or fine-tuning). Strong SQL and Python skills; experience with modern ELT/orchestration tools (dbt, Airflow, Spark, or equivalent). Deep familiarity with at least one major cloud data platform (Snowflake, Databricks, BigQuery, Redshift, or Azure Synapse). Practical experience with major LLM provider APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock) and LLM application frameworks (LangChain, LlamaIndex, or similar). Solid understanding of data security, access control, and privacy practices for sensitive or regulated data. Track record of making and documenting architecture decisions independently, with minimal oversight. Strong written and verbal communication skills; able to explain technical tradeoffs to non-technical stakeholders. Experience defining systems of record across CRM, ERP, and support platforms, and integrating systems such as Salesforce, NetSuite, HubSpot, Zendesk, or Jira. Strong data modeling skills (dimensional modeling, medallion or equivalent) and experience standing up a data catalog and data quality testing. Preferred Experience in a regulated industry (healthcare, financial services, or compliance-driven environments). Experience fine-tuning or self-hosting open-source LLMs (e.g., Llama, Mistral) for cost or data-residency reasons. Prior experience building a data or AI function from scratch at a startup or scale-up. Familiarity with MLOps/LLMOps tooling for evaluation, monitoring, and guardrails (e.g., MLflow, Weights & Biases, Ragas, or custom eval harnesses). Experience with infrastructure-as-code and containerized deployments (Terraform, Docker, Kubernetes). Prior people-leadership or technical-lead experience, if

the role is expected to grow a team. What Success Looks Like In the first 6 months: core data warehouse/lake architecture is live, key data sources are ingested and m