Job Description
Description
Duration
3–6 Months (Extension / PPO based on performance)
About the Role
We are looking for GenAI Interns passionate about applying Large Language Models (LLMs) and AI agents to real-world medical imaging workflows such as radiology reporting, clinical summarization, decision support, and enterprise viewers.
You will work closely with product, engineering, and clinical SMEs to build production-grade AI features for RIS, PACS, VNA, and reporting platforms.
Key Responsibilities
🔹 GenAI & LLM Development
Build and fine-tune LLM-based pipelines for:
Radiology report generation & structuring
Clinical summarization from imaging + text inputs
Follow-up recommendations and impression drafting
Implement prompt engineering, RAG (Retrieval Augmented Generation), and agentic workflows
Integrate LLMs with structured + unstructured medical data
🔹 Medical Imaging & Reporting
Work with DICOM metadata, radiology reports, and non-DICOM clinical documents
Assist in developing AI-assisted structured reporting templates (SR / FHIR-aligned)
Build GenAI features that work alongside 2D/3D viewers and enterprise imaging platforms
🔹 Engineering & Integration
Develop APIs using Python / FastAPI
Integrate GenAI services with backend systems (RIS / PACS / VNA / Enterprise Viewer)
Work with vector databases (FAISS / Pinecone / Chroma) for semantic search
Participate in model evaluation, hallucination control, and clinical safety checks
🔹 Research & Innovation
Evaluate latest research in medical GenAI, multimodal AI, and healthcare LLMs
Prototype new AI workflows for:
Blind reads
AI second opinions
Quality & consistency checks in reports
Requirements
Eligibility
Final-year students or recent graduates in
Computer Science / AI / Data Science / Biomedical Engineering / Health Informatics
Technical Skills (Expected)
Must Have
Strong Python fundamentals
Hands-on experience with LLMs (OpenAI, Azure OpenAI, Gemini, LLaMA, etc.)
Understanding of prompt engineering and RAG
Basic API development experience (REST / FastAPI)
Good to Have
Familiarity with medical imaging concepts (DICOM, modalities, studies, series)
Experience with LangChain / LlamaIndex / Semantic Kernel
Knowledge of FHIR / HL7 / Clinical terminologies
Exposure to Docker, cloud (GCP/AWS/Azure)
Domain Knowledge (Nice to Have)
Radiology workflows (ordering → acquisition → reporting → distribution)
RIS / PACS / VNA ecosystems
Clinical report structuring and terminology (Impression, Findings, Conclusion)
What You’ll Learn
Real-world GenAI deployment in regulated healthcare environments
How to build safe, explainable, and scalable AI for clinical use
End-to-end AI product development—from prototype to production
Working with imaging data, reports, and enterprise healthcare systems
What We’re Looking For
Curious, self-driven problem solvers
Passion for AI + healthcare impact
Ability to convert ambiguous clinical problems into structured AI solutions
Strong communication and documentation skills
What We Offer
Hands-on exposure to production healthcare AI systems
Mentorship from industry experts in medical imaging & AI
Opportunity for Pre-Placement Offer (PPO)
Certificate & recommendation letter upon successful completion
