GenAI Software Engineer – Medical Imaging, RIS & Healthcare Services

Role Overview

We are looking for a GenAI Software Engineer to design, build, and scale AI-powered capabilities across Medical Imaging (PACS/VNA/Viewers), RIS, and healthcare service platforms.
This role sits at the intersection of LLMs, medical imaging workflows, clinical data, and enterprise SaaS systems, enabling automation, insights, decision support, and intelligent user experiences for clinicians, operations teams, and executives.

Key Responsibilities

GenAI & AI Platform Development

• Design and implement LLM-based solutions for healthcare use cases such as:

  • Radiology report generation, summarization, and structuring

  • Clinical workflow automation (RIS scheduling, protocoling, triage)

  • Intelligent search across imaging, reports, and clinical documents

  • AI-assisted quality checks, utilization analytics, and insights

• Build agentic AI workflows (tool use, multi-step reasoning, RAG pipelines).

• Develop prompt engineering, prompt versioning, and evaluation frameworks.

Medical Imaging & RIS Integration

• Integrate GenAI solutions with PACS, VNA, RIS, and Enterprise Viewers.

• Work with DICOM, DICOMweb (WADO, QIDO, STOW) and non-DICOM clinical data.

• Enable AI outputs as:

  • Structured reports

  • Viewer overlays/annotations

  • Workflow recommendations and alerts

• Collaborate with imaging, clinical, and product teams to ensure clinical relevance and safety.

Backend & API Engineering

• Build scalable REST / gRPC APIs for AI services.

• Implement secure ingestion pipelines for imaging metadata, reports, and clinical documents.

• Design event-driven and microservices architectures for AI workloads.

• Optimize performance, latency, and cost for AI inference at scale.

Data, RAG & Knowledge Systems

• Design and manage RAG pipelines using clinical documents, SOPs, imaging metadata, and reports.

• Implement vector databases and hybrid search.

• Ensure data provenance, grounding, and traceability for AI outputs.

Governance, Security & Compliance

• Implement AI governance, explainability, and auditability.

• Ensure compliance with HIPAA, GDPR, and healthcare data security standards.

• Apply role-based access control (RBAC) and tenant isolation for enterprise SaaS.