Role: 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, DALL-E, 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).