Artificial Intelligence Engineer

Job description

**Role**: Data Scientist Generative & Agentic AI (Healthcare Domain) **Educational Qualification**: ME / BE / MCA **Experience Required**: 2+ Years **Shifts**: Day Shift **Skills & Responsibilities** **Experience**: 2+ 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.**Role & responsibilities**