Description : Data Scientist - Generative & Agentic AI (Healthcare Domain).
Educational Qualification : ME / BE / MCA.
Experience Required : 3-5 Years.
Shifts : Day Shift.
Mode : Hybrid.
Skills & Responsibilities :
Experience :
3+ years of experience in Machine Learning, Deep Learning, or Generative AI, with a strong focus on healthcare software product development and medical coding automation.
Programming & Frameworks :
- Proficient in Python, with hands-on experience using Pandas, NumPy, and OOPs concepts.
- Practical experience with PyTorch, TensorFlow, Keras, and Hugging Face Transformers.
- Familiar with writing optimized SQL queries for large-scale structured clinical data.
Healthcare-Specific AI :
Strong understanding of medical coding standards (ICD, CPT, SNOMED), EHR systems, and clinical document processing.Exposure to HL7, FHIR APIs, and privacy regulations like HIPAA is an added advantage.Generative AI & NLP :
Experience working with LLMs, GANs, VAEs, and Diffusion Models in healthcare use cases (e.g., clinical summarization, automated coding, documentation assistance).Familiar with Azure OpenAI, AWS Bedrock, DALLE, and Stable Diffusion platforms.Strong grasp of NLP techniques such as Named Entity Recognition (NER), token classification, contextual embeddings, and deep learning models like RNN,LSTM, GRU.Agentic AI & Autonomous Workflows :
Experience or familiarity with building agentic systems using LangChain, AutoGen, or CrewAI for orchestrating multi-step tasks (e.g., claim validation, document parsing).Ability to integrate autonomous agents with tool-based systems and APIs to enhance workflow efficiency.Machine Learning & Statistical Modeling :
Expertise in supervised and unsupervised ML, including Random Forest, SVM, Boosting, Bagging, Regression, and Clustering methods.Strong capability in feature engineering, model training, and cross-validation for healthcare data.MLOps, Deployment & Data Integration :
Experience with cloud platforms such as AWS, Azure, or GCP for scalable ML model deployment.Familiarity with MLOps practices, CI / CD pipelines, Docker, Kubernetes, and model versioning.Hands-on experience with Apache NiFi for data ingestion, integration, and workflow automation, including designing NiFi flows for structured / unstructured clinical data and seamless integration with downstream ML models.Proficient with Linux systems and GPU-based ML workflows.Research, Compliance & Ethics :
Experience contributing to AI research, open-source projects, or Kaggle competitions focused on healthcare or NLP.Awareness of AI ethics, bias mitigation, explainability techniques, and safe deployment of AI in clinical settings.Soft Skills & Collaboration :
Proven ability to work independently and in agile teams with product managers, clinical SMEs, and backend engineers.Effective communication for presenting results, writing technical documentation, and supporting regulatory submissions.Knowledge of computer vision is a plus for multimodal applications (e.g., diagnostics, image-text synthesis)(ref : hirist.tech)