Description

Role Overview:

We are seeking a visionary and hands-on Machine Learning & Generative AI Architect to lead the design, development, and deployment of cutting-edge AI/ML and GenAI solutions in the healthcare and biotech domain. The ideal candidate will have deep expertise in transformer-based architecture, generative models (LLMs, GANs, Diffusion Models), and MLOps, with a strong understanding of regulatory compliance (e.g., HIPAA) and healthcare data systems.

Key Responsibilities:

Generative AI & LLMs

• Architect and implement transformer-based generative models tailored for biomedical data, optimizing training pipelines for domain-specific nuances.
• Compare and evaluate generative models (GANs, VAEs, Diffusion Models) for synthetic medical image generation, selecting the most suitable based on use case.
• Design scalable GenAI systems for generating synthetic patient records with built-in HIPAA compliance and data anonymization.
• Fine-tune pre-trained LLMs for domain-specific applications such as drug discovery, clinical trial summarization, and medical literature analysis.
• Develop strategies to mitigate hallucinations in LLMs, especially for clinical decision support systems.

System Architecture & Integration

• Design GenAI-powered platforms that integrate seamlessly with existing EHR systems, supporting real-time inference and secure data exchange.
• Define and implement APIs for GenAI services optimized for multi-cloud and hybrid environments, ensuring scalability and interoperability.
• Ensure robust model versioning, traceability, and reproducibility in compliance with regulatory standards.

MLOps & Deployment

• Lead the development of CI/CD pipelines and MLOps workflows for model training, evaluation, and deployment.
• Collaborate with data scientists and engineers to optimize model performance using TensorFlow, PyTorch, and Hugging Face.
• Manage cloud-native microservices using Python, FastAPI, and container orchestration tools (e.g., Kubernetes).

Required Skills & Qualifications:

• Strong foundation in AI/ML, deep learning, and GenAI architectures.
• Hands-on experience with LLMs, RAG systems, LangChain, LangGraph, and vector databases.
• Proficiency in Python, FastAPI, and cloud platforms (AWS, Azure, GCP).
• Experience with MLOps tools and practices (MLflow, Kubeflow, CI/CD).
• Deep understanding of healthcare data standards (FHIR, HL7) and compliance frameworks (HIPAA, GDPR).
• Excellent communication and leadership skills

Key Skills

Education

Any Graduate