Jobs at RhythmX AI

Own the roadmap and delivery of an AI-first healthcare data platform spanning clinical and revenue cycle ingestion, interoperability, canonical data modeling, workflow orchestration, APIs, and AI-ready pipelines. Partner closely with engineering, AI/ML, RCM, coding, and finance teams to build scalable systems supporting claims, remittances, patient timelines, denial prediction, coding automation, and reimbursement intelligence.

Leads DevOps, SRE, DevSecOps, and platform reliability across Azure and AWS. Owns Kubernetes infrastructure, CI/CD, GitOps, databases, observability, security, distributed systems, incident response, disaster recovery, and FinOps. Builds and mentors engineering teams while improving availability, deployment safety, MTTR, DORA metrics, compliance, and cloud cost efficiency. This hands-on leadership role requires deep cloud-native, infrastructure, automation, and production troubleshooting expertise.

Build and operate secure, reliable cloud-native deployments for a healthcare SaaS platform. Own CI/CD pipelines, GitOps workflows, Kubernetes deployments, Azure infrastructure, infrastructure-as-code, observability, database operations, security automation, and cloud cost optimization. Troubleshoot production systems, participate in on-call rotations, improve deployment and recovery processes, and reduce operational toil through automation.

Build and optimize large-scale healthcare data pipelines supporting AI applications. Manage EHR and clinical data using FHIR, HL7, Epic Clarity, Airflow, databases, data lakes, and vector databases. Develop AI-enabled retrieval and orchestration workflows involving RAG, GraphRAG, LLMs, and tool calling. Collaborate with data science and backend teams, validate generative AI systems, maintain secure data exchanges, and improve scalability, accuracy, latency, and performance.

Build and lead AI quality validation frameworks for clinical insights, combining deterministic tests, semantic metrics, model-based grading, and automated hallucination and safety detection. Develop Python-based test harnesses, validate data pipelines and APIs, investigate failures across code, data drift, and model behavior, and establish ground-truth datasets and clinical evaluation rubrics with data science and product teams.

Lead the design, development, and optimization of scalable healthcare data pipelines supporting AI applications. Manage EHR, FHIR, HL7, clinical guideline, and other healthcare data while ensuring secure, validated, and compliant processing. Orchestrate data and knowledge workflows using Airflow, Temporal, RAG, GraphRAG, and related technologies. Collaborate with data science and backend teams, guide GenAI production implementations, and improve retrieval accuracy, latency, scalability, and data quality.