Data & AI Platform Architect

ProviderTrust and Role 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.

What Success Looks Like
In the first 6 months: core data warehouse/lake architecture is live, key data sources are ingested and modeled, and a first internal LLM use case (e.g., an internal knowledge assistant) is in production.

In the first 12 months: the data platform reliably serves analytics and AI workloads across the company, a customer-facing LLM feature has shipped with monitoring/guardrails in place, and governance standards for data and AI usage are documented and adopted.

Job Requirements

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 It's Like to Work Here

At ProviderTrust, we recognize that experience is built in many ways. If you have relevant skills that are not reflected in your resume or your experience doesn’t match our exact requirements, we welcome your candidacy and encourage you to share more. We champion building a team that embodies empathy, equity, respect, and inclusivity while actively supporting our community, clients, partners, and friends. We value differences of opinion and embrace unique perspectives. We desire an environment that allows all team members to bring their whole selves to work unashamedly. We carefully consider every application and will either move forward with you, find another team that might be a better fit, keep in touch for future opportunities, or thank you for your time. ProviderTrust is an equal opportunity employer.

To be great at ProviderTrust, we find our team members have these things in common:

• Gain energy from working in a fast-paced, creative environment
• Decision-making that employs a blend of data-driven insights and intuition
• Ability to multitask and handle multiple projects concurrently
• Resilience and positivity, able to address setbacks and bounce back quickly
• Resourcefulness, discovering creative ways to get things done
• Joy in making an immediate and positive impact
• Diverse interests that are welcomed and extend beyond our organization

Things That Make Us A Great Place To Work

• Hybrid schedule: 2 days in office
• 16-week paid primary caregiver with a 2-week phase-back leave policy
• 4-week paid secondary caregiver leave policy
• We're genuinely curious about AI. Team members are encouraged to experiment and share what they learn
• Competitive base salary and incentive package with 401k matching, HSA employer contribution, and company-paid life and disability insurance
• Medical, dental, and vision benefits: PT pays 80% of your premiums. We also offer access to a range of free mental health and well-being resources.
• Unlimited PTO, 11 paid holidays, and a flexible work schedule 
• Internal professional growth, development, and mobility
• Daily all-company morning huddles to sync up across the business
• In-office experience: fully stocked kitchen, ergonomic desk setup, and many celebrations!
• Remote experience: home office set-up with technology provided, remote-friendly meetings and celebrations, and interest-specific Slack channels for connecting across teams 
• Fitness stipend and cell phone reimbursement
• Modern Healthcare Best Places to Work (2021-2026)
• The Tennessean Top Work Places (2022-2026)