Key skills
Data EngineeringHealthcare Common Data Models OMOPHealthcare Common Data Models FHIRHealthcare Common Data Models PCORnetMedical Ontologies UMLSMedical Ontologies SNOMED CTMedical Ontologies RxNormBig Data Tooling SparkBig Data Tooling IcebergBig Data Tooling EMRPythonSQLNatural Language ProcessingSemantic SearchEntity RecognitionConcept NormalizationEmbedding PipelinesInferential StatisticsCohort MethodologyAI/ML Product DevelopmentAIMLNLPClaudeRAGData LakeAnalyticsSparkAWSAzureGCPLeadershipMentoring
About this role
Medeloop is a technology company focused on transforming healthcare through AI. They are seeking a Senior AI Data Engineer to architect the data backbone for their AI platform, working closely with data scientists and product teams to manage and enhance healthcare data workflows.
Responsibilities:
- The healthcare data lake: curating, extending, and evolving it through new concepts, derived variables, and data models that directly inform our AI engines and customer products
- AI-native data workflows: designing and operating AI-powered pipelines (using tools like Claude Code and agent frameworks) to automate harmonization, cleaning, quality checks, and summarization at scale
- NLP and semantic infrastructure: building pipelines for entity extraction, concept normalization, embedding-based retrieval, and semantic search that power the AI Scientist platform
- Novel data extraction approaches: experimenting with and building new methodologies for working with unstructured clinical data, not just applying existing playbooks
- Research-grade data products: delivering analytical samples, cohorts, and final datasets that withstand scientific scrutiny and are actively used by researchers and customers
- Data governance and observability protocols: including access controls, PHI/PII handling, data classification, compliance, monitoring, alerting, data freshness, and comprehensive documentation to enable self-service capabilities
Requirements:
- 3+ years of relevant data engineering or data management within an analytics-driven organization, with end-to-end ownership from raw ingestion to final data product
- Deep hands-on experience with healthcare CDMs (OMOP, FHIR, PCORnet) — designing or extending them, not just querying
- Knowledge of medical ontologies: UMLS, SNOMED CT, RxNorm
- Experience with big data, data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, embedding workflows, and other AI/ML workloads. Experience in big data tooling such as Spark, Iceberg, EMR
- Fluent in Python and SQL; comfortable across structured and unstructured data
- Proven NLP experience: semantic search, entity recognition, concept normalization, embedding pipelines
- Strong grasp of inferential statistics and cohort methodology to be a real partner to data scientists and customers (as part of onboarding)
- Experience contributing to an AI/ML product, especially in automated research or scientific discovery
- Experience mentoring other engineers and providing technical leadership
- Multi-cloud experience (AWS, Azure, GCP)
- Authorship or contribution to peer-reviewed publications or technical reports