AI Engineer - Health care

Role Overview

Join AI Nexus Innovations Hub as an AI Engineer to build clinical AI for the future of healthcare. You will work on a real Hospital Management System (Medorbit.ai) where your models help doctors make faster, sharper decisions for real patients. This role involves working on document intelligence, LLMs, medical imaging, and predictive analytics within a live HMS product.

Responsibilities

• Build AI Lab Report Intelligence pipelines to extract test values and trends from digital and scanned PDFs.
• Generate concise, doctor-ready summaries and predictive summaries highlighting deteriorating markers.
• Develop AI-assisted consultation suggestions and surface subtle out-of-range drifts.
• Train and fine-tune deep learning models for AI-Assisted X-Ray Analysis to detect abnormalities.
• Integrate imaging models into the HMS using Grad-CAM heatmaps for explainability.
• Work with LLMs, prompt engineering, RAG, and AI agents to build intelligent clinical workflows.
• Integrate AI models via REST APIs and build data pipelines for training and inference.
• Collaborate with the founding team on architecture and product decisions.

Requirements

• B.Tech / B.E. / M.Tech / M.S. in Computer Science, AI/ML, Data Science, Biomedical Engineering, or a related field.
• Strong fundamentals in Python and at least one ML framework like PyTorch, TensorFlow, or scikit-learn.
• Understanding of ML concepts including supervised/unsupervised learning, neural networks (CNNs), and NLP basics.
• Familiarity with Generative AI, LLMs, or prompt engineering.
• Solid problem-solving skills and a builder's mindset.
• Excellent communication skills and a self-starter mentality.

Nice to Have

• Projects in medical imaging or document AI (OCR).
• Exposure to LangChain, Hugging Face, OpenAI/Anthropic APIs, or vector databases.
• Experience with Tesseract, Azure/AWS Document AI, or LayoutLM.
• Knowledge of healthcare data standards like FHIR, HL7, DICOM, or ABDM.
• Knowledge of Java, Scala, or Apache Spark.
• Cloud platforms (AWS preferred), Docker, Git, and CI/CD pipelines.