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.

Required Skills & Experience

Core Technical Skills

• Min 7 - 12yrs of relevant experience

• Strong experience with Python (primary) and/or Java / Go / Node.js.

• Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, open-source models).

• Experience with LangChain, LlamaIndex, or similar AI orchestration frameworks.

• Solid backend engineering experience with FastAPI / Spring Boot / Express.

• Knowledge of Docker, Kubernetes, and cloud-native architectures.

Healthcare & Imaging (Must-Have or Strong Plus)

• Experience with Medical Imaging systems (PACS, VNA, Viewers) or RIS / HIS.

• Understanding of radiology workflows and clinical data models.

• Familiarity with HL7, FHIR, DICOM, and healthcare interoperability.

Data & AI Engineering

• Experience with vector databases (Pinecone, FAISS, Milvus, Weaviate).

• Knowledge of ML model evaluation, hallucination mitigation, and grounding techniques.

• Experience building production-grade AI systems, not just prototypes.

Nice-to-Have Skills

• Experience generating DICOM SR / GSPS or AI annotations.

• Exposure to computer vision models in medical imaging.

• Experience with streaming architectures (Kafka, Pub/Sub).

• Knowledge of FinOps / cost optimization for AI workloads.

• Prior experience in regulated SaaS platforms.

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

• Healthcare or AI-focused certifications are a plus.

What You’ll Work On

• AI-assisted radiology and imaging workflows

• Intelligent RIS automation and operational insights

• Enterprise Viewer AI integrations

• Scalable GenAI platforms for healthcare services

• Next-generation clinical and executive decision support systems