Principal AI Engineer

🕒 Junho 29

🔔 Pennsylvania – Remoto

🔔 Pennsylvania – Remote

💵 $180.000 - $200.000 / ano

⏰ Tempo Integral

🔴 Especialista

🤖 Engenheiro de IA

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MRO

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

🏥 Saúde

☁️ SaaS

📋 Conformidade

💰 Series unknown em 2019-11

Healthcare • SaaS • Compliance

A MRO é uma empresa de software de saúde empresarial que fornece uma Plataforma de Intercâmbio de Dados Clínicos (CDXP) e soluções modulares para curar, normalizar e trocar dados clínicos com segurança entre provedores, pagadores, registros e solicitantes de dados clínicos. A MRO ajuda hospitais, sistemas de saúde, práticas ambulatoriais e pagadores a otimizar a liberação de informações, fluxos de trabalho de conformidade, relatórios de qualidade/MIPS, rastreamento de ACO e inteligência clínica conectando-se a centenas de prontuários eletrônicos de saúde (EHRs) distintos, reduzindo a carga administrativa e acelerando a entrega de cuidados focados no paciente. A empresa enfatiza a segurança e a conformidade regulatória e foi reconhecida com prêmios Best in KLAS por Liberação de Informação.

Descrição

• Own the end-to-end technical vision for MRO Prodigy's AI layer — a production system that uses RAG, generative AI, and structured data reasoning to automate answers to Healthcare Registry questionnaires. • Define the AI roadmap for Prodigy, balancing near-term customer commitments against foundational capability investments that scale the product to enterprise maturity. • Evaluate and make build/buy/integrate decisions for AI capabilities — foundation model selection, embedding strategies, retrieval architectures, and orchestration frameworks — and own the consequences of those decisions. • Serve as the technical authority on all AI design decisions for Prodigy; produce architecture decision records, set standards, and ensure the architecture is defensible, auditable, and extensible. • Architect and evolve Prodigy's multi-modal retrieval pipeline, combining unstructured clinical document ingestion with structured EHR/FHIR data to surface accurate, citation-backed answers to registry questionnaire items. • Design and refine the answer generation layer — prompt engineering, context construction, grounding strategies, and output formatting — ensuring generated answers are clinically accurate and audit-ready. • Own the question routing and data source classification logic that maps registry questions to the right retrieval path, structured data field, or generation strategy. • Build and maintain the answer validation and confidence scoring framework, defining the statistics and quality thresholds that govern when answers are auto-accepted versus routed for human review. • Stay hands-on and close to the work: run direct ideation and feedback loops with Prodigy's end users (abstractors, registry, and quality teams) and with production analytics and monitoring systems — turning real usage signals into prioritized improvements that demonstrably move value, not just model metrics. • Evolve the feedback loop architecture that captures human corrections and routes them into continuous model improvement — ensuring Prodigy gets measurably better with every customer interaction. • Define the evals framework for Prodigy: how accuracy is measured, how regression is detected, and what signals trigger retraining or prompt revision. • Establish guardrails for hallucination detection and factual grounding specific to clinical registry use cases, where answer accuracy has direct downstream compliance implications. • Architect AI infrastructure across GCP (Vertex AI, BigQuery, Dataflow) and AWS (Bedrock), ensuring the pipeline is scalable, cost-efficient, and operationally observable. • Collaborate with data engineering to maintain high-quality, well-governed clinical and FHIR data inputs; define feature engineering and chunking strategies that optimize retrieval precision. • Define MLOps standards for Prodigy: model versioning, deployment gates, rollback procedures, drift monitoring, and audit trail requirements consistent with HIPAA compliance. • Act as the AI technical mentor for the Prodigy squad and adjacent engineering teams — guiding developers on RAG patterns, LLM integration, responsible AI practices, and clinical data handling. • Collaborate with Security and Compliance to ensure Prodigy's AI layer meets HIPAA requirements, including PHI handling in prompts, data residency, and model audit logging. • Foster AI literacy across the broader engineering organization, helping teams understand when and how to apply AI safely in a regulated healthcare context. • Partner with Product Management to translate registry workflow complexity and customer feedback into technically sound AI capability improvements.

🎯 Requisitos

• Bachelor's in Computer Science, AI/ML, or related field; Master's or PhD preferred - or equivalent depth proven through shipped AI systems. • Strong ML / data science / statistics theory foundation with the ability to read research, assess applicability, and execute. • Hands-on LLM integration: prompt engineering, grounding, citation, hallucination mitigation, and output validation at clinical accuracy standards. • Experience with LangChain, LlamaIndex, or equivalent orchestration frameworks. • Built confidence scoring and auto-acceptance thresholds that govern when answers route to human review. • Designed human-in-the-loop feedback systems that capture corrections and feed them back into model improvement. • Production experience building RAG pipelines — document ingestion, chunking, embedding model selection, vector store management, and retrieval evaluation. • Full ML lifecycle ownership in production: versioning, deployment gates, drift monitoring, rollback, and audit trails. • Strong Python and software engineering fundamentals — CI/CD, testing, code review. • Hands-on with vector databases (pgvector, Pinecone, Weaviate, or equivalent) and hybrid search. • Built evals frameworks that measure accuracy, precision, recall, and F1 to inform product decisioning. • Solid AWS and GCP experience: Bedrock, SageMaker, Vertex AI, BigQuery, Dataflow. • Azure familiarity a plus. • Experience building pipelines over mixed unstructured and structured data sources. • Clinical NLP or healthcare AI experience — medical terminology, document structure, and regulated accuracy standards are not new territory. • Knows how HIPAA applies to AI systems specifically: PHI in prompts and embeddings, data residency, audit logging, de-identification. • Familiar with AI governance in practice: bias detection, explainability, responsible AI in compliance-sensitive contexts. • Has owned AI technical vision before — not just contributed to it. • Can write an ADR, set an engineering standard, and make it stick across teams. • Communicates tradeoffs clearly to both engineers and non-technical stakeholders. • Track record of mentoring engineers and raising AI maturity on a team.

🏖️ Benefícios

• Eligible employees may also receive an annual cash bonus • comprehensive benefits offering, including medical, dental, vision, life insurance, and a 401(k) plan.