Technical Product Manager – AI Platform & Interoperability

The role

You will drive the development of core platform capabilities for an AI-first healthcare data platform. This includes managing healthcare data ingestion, data normalization, and workflow orchestration to power AI-driven insights.

What you will do

• Define and deliver scalable ingestion pipelines for HL7 v2, FHIR R4, and other clinical data formats
• Drive integration with interoperability platforms
• Own the design and evolution of a canonical clinical data model
• Define mapping frameworks for terminologies and coding systems
• Define systems for event-driven and batch data processing pipelines
• Partner with engineering to implement scalable workflows using orchestration frameworks
• Define APIs for data ingestion, transformation, and downstream consumption
• Enable data pipelines that support clinical decision support and recommendation systems
• Translate platform vision into product backlog including epics, user stories, and acceptance criteria
• Prioritize and sequence work across ingestion, processing, and platform services

Required qualifications

• 5–8+ years of product management experience in a technical domain
• Strong experience building platforms, APIs, or data infrastructure products
• Ability to work deeply with engineers on architecture, system design, and trade-offs
• Proven track record of delivering scalable systems from concept to production
• Strong understanding of data modeling and schema design
• Strong understanding of ETL / ELT pipelines
• Strong understanding of batch and streaming architectures
• Experience working with PostgreSQL or similar relational databases
• AI-first, AI-fast approach to product development
• Strong ownership and bias for execution
• Comfortable operating in ambiguity and solving complex problems
• Deep curiosity and willingness to engage at technical depth

Nice to have

• Experience with HL7 v2, FHIR R4, and healthcare data exchange standards
• Familiarity with interoperability engines (e.g., Redox, Mirth, Rhapsody)
• Understanding of clinical data domains and workflows
• Workflow orchestration frameworks (e.g., Temporal)
• Graph databases (e.g., Neo4j, Amazon Neptune)
• Cloud platforms (Azure preferred, AWS acceptable)
• Distributed systems and event-driven architectures
• Exposure to AI/ML or LLM-based systems
• Understanding of structured + unstructured data pipelines
• Understanding of Retrieval-augmented generation (RAG)

About the company

GW RhythmX uses AI to help 150+ health systems deliver the right care. As a Symphony AI portfolio company, it combines startup agility with enterprise-scale infrastructure.