Principal AI Architect
Posted
Summary
Hands-on principal-level AI architect who owns the whole AI function at ElevatePFS — technical vision, architecture, and quality/cost of AI solutions — designing and building agentic LLM, RAG, knowledge-graph, and document-intelligence systems on a Databricks platform for healthcare revenue-cycle workflows. Path to Head of AI Engineering.
Elevate Patient Financial Solutions has an exciting opportunity open for a Principal AI Architect.
Job Summary
The Principal AI Architect is a hands-on individual contributor who owns the AI technical function at ElevatePFS, including the technical vision, the architecture, and the quality and cost of the solutions the company builds with AI. The role serves as the company's deepest AI technical authority and reports to the VP, AI & Automation.
ElevatePFS is building its AI, automation, and intelligence capabilities in-house on a centralized Databricks data platform. Business and product leaders define the problems worth solving. The Principal AI Architect determines whether AI is the right tool for each problem and, if so, designs the solution, builds it, and runs it in production. Training foundation models from scratch is the only work outside the scope of the role. The position is expected to grow into Head of AI Engineering as the team develops.
Essential Duties and Responsibilities
- Set the technical vision and roadmap for AI with the VP, AI & Automation, giving leadership a clear view of feasibility, value, cost, and risk.
- Partner with the Product delivery team to turn workflow problems and improvement opportunities identified in the field into scoped, feasible AI and application opportunities.
- Decide which parts of each solution should be deterministic (rules, parsers, schema validation, SQL, classical ML) and which should be dynamic (LLM reasoning, agentic tool use), using LLMs where inputs are ambiguous and plain code elsewhere.
- Recommend build versus buy and closed versus open models for each solution, and track new models and tooling.
- Design single-agent and multi-agent systems, including routing, planner/executor patterns, tool calling and MCP, state and memory, durable execution, retries, idempotency, human approval gates, and tool permission scoping.
- Define with Product what the application needs to support AI in daily use, such as changed screens, wizards, intelligent interfaces, and approval steps.
- Build and tune retrieval-augmented generation end to end, including parsing, chunking, embedding selection and fine-tuning, hybrid search, reranking, query rewriting, vector search with Delta index sync, and grounded citations.
- Build knowledge graphs and ontology-driven retrieval, including entity resolution and GraphRAG-style hybrids, in partnership with the Data organization.
- Develop document intelligence for contracts, payment files, and correspondence, including OCR, layout-aware parsing, table and field extraction, and multimodal models.
- Design the Databricks data layout that AI depends on, including medallion architecture, Delta table design, liquid clustering, Change Data Feed, Unity Catalog governance and lineage, and feature and serving tables.
- Select models by task, accuracy, latency, cost, and data-handling constraints, fine-tune where it pays off (SFT, LoRA/QLoRA, preference tuning), and distill into smaller, lower-cost models.
- Deploy and serve models on Databricks Model Serving and self-hosted inference such as vLLM, and manage the lifecycle in MLflow, including tracking, tracing, registry, release management, and CI/CD for models, prompts, and agents with shadow, canary, and A/B rollouts.
- Build evaluation and monitoring tied to business outcomes, including golden datasets, field-level precision, recall, and F1, retrieval metrics, groundedness, LLM-as-judge calibrated to human labels, release-gating regression suites, and production drift and agent-trace monitoring.
- Own token and compute spend through routing and cascading, prompt and semantic caching, context compression, batch inference, smaller models, an AI gateway, and cost attribution per agent and workflow.
- Measure ROI and adoption for every AI initiative and recommend whether to scale, rework, or retire it.
- Defend against prompt injection, tool-based data exfiltration, and over-permissioned agents through least privilege, guardrails, PII/PHI redaction, and full audit logging.
- Work with the AI Center of Excellence, Information Security, Compliance, and Legal on model approval and data-handling standards, including standards for PHI.
- Lead the technical build from design through production, write the specs, patterns, and standards for everyone building AI at ElevatePFS, and review code, prompts, and evaluations from employees, contractors, and vendors.
- Hand off completed designs to the Product delivery team, who own implementation and adoption, and provide the specs and guidance they need.
- Keep the intelligence the company builds documented, understood, and owned internally.
- Mentor engineers, help recruit the AI engineering team that forms around the role, and explain technical tradeoffs clearly to executives.
- Other duties as assigned.
Qualifications and Requirements
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or abilities.
- 10+ years of experience in software, data, or ML engineering, including 7+ years in machine learning with increasing scope.
- Strong current Python and SQL skills, with the ability to build, test, and debug AI systems personally.
- Production experience with LLM agents, retrieval-augmented generation, knowledge graphs, closed and open-weight models, fine-tuning, and model deployment.
- Record of proving ROI, controlling AI cost, and choosing well between deterministic and LLM-driven design.
- Experience securing AI systems in regulated or sensitive-data environments.
- Record of setting technical direction that others executed, including across employees, contractors, and vendors.
- Preferred: Healthcare, healthcare IT, or revenue cycle management background, including HIPAA and PHI-compliant AI deployment.
- Preferred: Knowledge graph or ontology experience, and document intelligence or claims and contract extraction experience.
- Preferred: Deep Databricks and MLflow experience, including Delta, Unity Catalog, Model Serving, and Vector Search.
- Preferred: Azure, event-driven architecture, and workflow automation or RPA experience.
- Preferred: Experience standing up an AI Center of Excellence or internal AI platform.
Benefits
ElevatePFS believes in making a positive impact not only within our industry but also with our employees – the organization’s greatest asset! We take pride in offering comprehensive benefits in a vast array of plans that contribute to the present and future well-being of our employees and their families.
- Medical, Dental & Vision Insurance
- 401K (100% match for the first 3% & 50% match for the next 2%)
- 15 days of PTO
- 7 paid Holidays
- 2 Floating holidays
- 1 Elevate Day (floating holiday)
- Pet Insurance
- Employee referral bonus program
- Teamwork: We believe in teamwork and having fun together
- Career Growth: Gain great experience to promote to higher roles
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training. This pay scale is not a promise of a particular wage.
The job description does not constitute an employment agreement between the employer and Employee and is subject to change by the employer as the needs of the employer and requirements of the job change.
ElevatePFS is an Equal Opportunity Employer