AI Research Scientist - Deep Tech / Healthcare AI (GenAI, LLMs, Multimodal AI)
Skills:
Artificial Intelligence | Generative AI | Large Language Models (LLMs) | Small Language Models (SLMs) | NLP | Deep Learning | Multimodal AI | Healthcare AI | Model Optimization | Research & Experimentation
Department: AI Research / Advanced Analytics
Employment Type: Full Time
Work Mode: Onsite / Hybrid / Remote
Experience: 4-10 Years
About the Role
We are seeking a highly skilled AI Research Scientist to drive cutting-edge research and development in Generative AI, Large Language Models (LLMs), and domain-specific AI systems, particularly within deep-tech and healthcare AI applications.
This role involves designing and optimizing domain-adapted AI models to solve complex real-world problems across areas such as clinical intelligence, diagnostics, medical NLP, and multimodal healthcare data processing. You will work at the intersection of research and production, contributing to the development of scalable, safe, and high-performance AI systems.
Key Responsibilities
AI Research & Model Development
- Conduct advanced research in NLP, deep learning, and multimodal AI
- Design and develop domain-specific LLMs and SLMs for healthcare and deep-tech use cases
- Work on foundation model training, fine-tuning, and optimization
- Develop novel approaches to improve model performance, robustness, and efficiency
Generative AI & LLM Engineering
- Build and optimize LLM-based applications using transformer architectures
- Implement:
- Supervised fine-tuning
- Reinforcement Learning from Human Feedback (RLHF)
- Pre-training and post-training optimization techniques
- Apply techniques such as LoRA, QLoRA, and parameter-efficient fine-tuning (PEFT)
- Design and deploy RAG (Retrieval-Augmented Generation) pipelines
Multimodal & Healthcare AI Systems
- Develop AI systems integrating:
- Text (clinical notes, reports)
- Imaging (radiology, pathology)
- Structured medical data
- Build models for:
- Clinical decision support
- Diagnostic insights
- Medical knowledge extraction
- Ensure models meet clinical-grade reliability and compliance standards
Data Engineering & Model Training
- Process and curate large-scale healthcare datasets
- Design data pipelines for:
- Data ingestion
- Preprocessing
- Feature engineering
- Ensure high-quality training data and bias mitigation
Model Evaluation & Optimization
- Evaluate models using domain-specific metrics and benchmarks
- Perform:
- Model validation
- Error analysis
- Continuous optimization
- Improve model safety, interpretability, and explainability
Collaboration & Production Integration
- Work with cross-functional teams including:
- Data engineers
- Product teams
- Domain experts (clinical/research)
- Translate research into production-ready AI solutions
- Support deployment and scaling of AI models
Research & Thought Leadership
- Publish research in top-tier conferences and journals
- Stay updated with advancements in:
- AI/ML
- NLP and LLMs
- Healthcare AI
- Contribute to innovation through experimentation and prototyping
Required Qualifications
- Bachelor's/Master's/PhD in Computer Science, AI, Data Science, or related field
- 4-10 years of experience in AI/ML research and development
- Strong expertise in:
- NLP, deep learning, and transformer architectures
- LLMs, SLMs, and generative AI systems
- Experience with:
- Fine-tuning techniques (full fine-tuning, PEFT, LoRA, QLoRA)
- RLHF and model alignment methods
- Proficiency in:
- Python
- ML frameworks (PyTorch, TensorFlow)
- Hugging Face ecosystem
- Experience working with:
- Large-scale datasets
- Distributed training systems
Technical Skills
AI/ML & Deep Learning
- NLP, transformers, embeddings, multimodal models
- Model training, evaluation, and optimization
GenAI & LLM Frameworks
- LangChain, LangGraph, LlamaIndex, LangSmith
- RAG pipelines and vector databases
Programming & Data
- Python, SQL
- Data processing frameworks (PySpark, Pandas)
Cloud & Infrastructure
- AWS / Azure / GCP
- Model deployment and scalable inference systems
Responsible AI & Compliance
- Model safety, fairness, and bias mitigation
- Understanding of regulatory requirements in healthcare (preferred)
Good-to-Have
- Experience in healthcare AI, clinical data, or medical imaging
- Exposure to multimodal AI systems
- Knowledge of AI governance and regulatory frameworks
- Contributions to open-source or published research work
- Experience deploying AI systems in production environments
Professional Competencies
- Strong research mindset with problem-solving capabilities
- Ability to translate complex problems into AI solutions
- Strong communication skills for technical and non-technical audiences
- Ability to work in cross-functional and collaborative environments
- High ownership and innovation-driven thinking
- Adaptability to evolving technologies and research trends
Why This Role is High Impact
- Work on cutting-edge AI research and real-world healthcare applications
- Build next-generation GenAI and LLM systems
- Contribute to high-impact innovations in healthcare and deep-tech
- Opportunity to publish, innovate, and influence AI strategy
- Solve complex problems with large-scale, high-value data systems
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