AI/ML Platform Architect

• Help architect, design and implement ThinkBio.Ai AI insight and data platforms; design agentic architecture for the AI platform
• Evaluate and analyze foundation models for protein structure, clinical data, genomic data and small molecule data; fine tune foundation models. Design loss functions relevant to biological applications
• Design scalable data models for multi-modal biological data
• Create a componentized architecture with component level structured APIs
• Design and implement knowledge graphs for biological data; apply deep learning based methods to create representations from knowledge graphs
• Work closely with other team members and partners to identify most critical data centered challenges and address them using cutting-edge computational, statistical and machine learning applications

Requirements

• Ph.D. in Computer science, AI/ML, Mathematics/Statistics
• 10+ years’ experience and technical expertise in AI platforms, data platforms, deep learning models
• Solid understanding of computer science fundamentals
  • data structures: trees, directed and undirected graphs, hash tables, heaps
  • algorithms: search, sort, rank, graph based algorithms
• Solid understanding of statistical/mathematical concepts
  • probability theory
  • correlation metrics
  • Bayesian probability
  • Statistical regression methods
• Good understanding of GPU architectures – CUDA
• Proficiency in Python, C, C++
• Expertise in AI frameworks: PyTorch, TensorFlow
• Exposure to agentic frameworks like LangChain and LlamaIndex
• Firm grasp of modern statistical methods and machine learning techniques, and their applications to large-scale, data
• Ability to manage projects with minimal supervision, using creative and analytical thinking.
• Ability to drive highly collaborative work across the organization and outside the company
• Excellent oral and written communication skills

Preferred Skills

• Experience and understanding of how bioinformatics and data science can best be applied to speed up drug discovery
• Basic understanding of biological concepts and a familiarity with drug development process
• Knowledge of bioinformatic tools and databases to analyze genomics and proteomics data.