Principal AI Scientist

Key Responsibilities

Research & Innovation

Design, prototype, and evaluate novel AI models for autonomous, agent-based systems in healthcare workflows.

Advance state-of-the-art NLU for clinical, operational, and administrative healthcare text.

Explore and implement new capabilities in reasoning, decision-making, and multi-modal understanding.

Healthcare-Focused AI Development

Adapt and fine-tune large language models to handle healthcare-specific language, regulatory requirements, and ethical considerations.

Create AI pipelines that can process structured and unstructured healthcare data (FHIR, HL7, clinical notes, claims data).

Contribute to domain-specific model architectures that improve clinical decision-making, revenue cycle management, and patient engagement.

Technical Leadership & Strategy

Technical Authority: Serve as the lead architect for AI/ML strategy, ensuring all models are explainable, robust, and compliant with healthcare regulations (HIPAA/HITRUST).

Research to Reality: Bridge the gap between theoretical research and practical engineering, ensuring prototypes are built with deployment and maintenance in mind.

Mentorship: Elevate the engineering team through rigorous code reviews, architectural design docs, and best practices in data science.

Data & Operational Excellence

Data Integrity: Oversee the processing of structured and unstructured healthcare data (FHIR, HL7, clinical notes) ensuring high data quality and security.

Validation: Implement rigorous model evaluation frameworks to detect bias and ensure clinical accuracy.

End-to-End System Architecture

Design & Deploy: Lead the full lifecycle of AI applications, from initial data exploration and model training to deploying secure, scalable services in a production environment.

Hybrid Modeling: Intelligently combine Classical ML (Random Forests, XGBoost, etc.) with Deep Learning and Generative AI to solve complex healthcare problems efficiently.

Agentic Frameworks: Architect agent-based reasoning systems to automate clinical workflows while maintaining strict "human-in-the-loop" safety standards.

Operational & Compliance Excellence

Implement rigorous model evaluation frameworks for accuracy, robustness, and fairness.

Ensure compliance with healthcare privacy and data security regulations (HIPAA, HITRUST).

Partner with engineering to move research prototypes into production environments.

Required Qualifications

PhD or Master’s in Computer Science, AI, Machine Learning, or related field.

5+ years of experience in AI/ML, with a strong portfolio of deployed models.

Proficiency in Python, R, and modern ML libraries.

Experience with GenAI, LLMs, and transformer architectures.

Skilled with AI development tools and frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, RAG systems).

Hands-on experience with autonomous AI agents and reasoning systems.

Strong track record applying AI to healthcare or life sciences use cases.

Familiarity with cloud platforms (AWS) and MLOps practices.

Preferred Skills

Multi-modal AI research experience (text, image, structured data).

Knowledge graph integration and symbolic reasoning expertise.

Proven ability to take AI research from concept to production.

Work location: Bangalore Hybrid

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