Job Description

We are seeking a GenAI Data Scientist with strong expertise in medical imaging, radiology workflows (RIS/PACS/VNA), and service-oriented healthcare platforms This role will focus on designing, training, and deploying Generative AI and Agentic AI solutions that improve clinical efficiency, operational intelligence, reporting automation, and service optimization across healthcare systems.

You will work closely with product managers, clinical SMEs, engineering teams, and cloud architects to translate healthcare problems into scalable AI-driven solutions.

Key Responsibilities

1. Generative AI & ML Development

• Design and develop LLM-powered and multimodal AI solutions for:

  • Radiology reporting automation

  • Imaging analytics and insights

  • Clinical decision support

  • Operational and service intelligence

• Build agentic AI workflows for tasks such as:

  • Study triage and prioritization

  • Report quality checks

  • Workflow optimization across RIS/PACS/VNA

• Fine-tune and evaluate LLMs and vision-language models using domain-specific medical datasets.

2. Medical Imaging & Radiology Domain Applications

• Work with DICOM, non-DICOM, and multimodal data (images, text, metadata, audio/video).

• Develop AI models for:

  • Image understanding and feature extraction

  • Metadata enrichment and study classification

  • Automated measurements and annotations

• Collaborate with clinical experts to ensure clinical relevance, safety, and interpretability of AI outputs.

3. Data Engineering & Model Lifecycle

• Build robust data pipelines for ingesting data from RIS, PACS, VNA, and service platforms.

• Perform data curation, labeling strategies, feature engineering, and dataset versioning.

• Implement model evaluation, monitoring, drift detection, and continuous learning pipelines

4. Healthcare Services & Operations Intelligence

• Apply AI to non-clinical service use cases, including:

  • Turnaround time (TAT) optimization

  • Resource utilization and scheduling

  • SLA adherence and predictive alerts

  • Revenue leakage and operational bottlenecks

• Build AI-driven dashboards, summaries, and conversational analytics for executives and operations teams.

5. Deployment, MLOps & Cloud

• Package and deploy AI models as APIs and microservices

• Implement MLOps best practices

  • CI/CD for models

  • Model registry and versioning

  • Observability and performance tracking

• Work on cloud-native deployments (AWS / GCP / Azure), ensuring scalability, security, and compliance.

6. Compliance, Ethics & AI Safety

• Ensure solutions comply with HIPAA, GDPR, and healthcare data privacy standards

• Implement explainability, auditability, and bias mitigation in AI models.

• Participate in AI governance and responsible AI initiatives.

Requirements

Required Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Biomedical Engineering, or related field.

Technical Skills

• Strong experience with Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).

• Hands-on experience with LLMs, prompt engineering, RAG, vector databases, and agent frameworks

• Knowledge of medical imaging standards (DICOM, HL7, FHIR preferred).

• Experience with SQL/NoSQL databases, data lakes, and analytics platforms.

• Familiarity with cloud platforms and containerization (Docker, Kubernetes).

Domain Knowledge

• Understanding of radiology workflows (order → acquisition → reporting → distribution).

• Familiarity with RIS, PACS, VNA, and enterprise imaging ecosystems

• Experience working with clinical, operational, or healthcare service data

Preferred Qualifications

• Experience building AI products for healthcare SaaS platforms.

• Exposure to computer vision in medical imaging (X-ray, CT, MRI, Ultrasound).

• Knowledge of FHIR-based APIs and interoperability standards.

• Experience with regulatory documentation and AI validation in healthcare.

• Prior experience working with clinicians or hospital IT teams.

• PhD would be an added Advantage.

Soft Skills

• Strong problem-solving and analytical mindset.

• Ability to communicate complex AI concepts to non-technical and clinical stakeholders

• Comfortable working in fast-paced, cross-functional product teams

• Strong ownership and bias toward execution.