Shayan Mousavi M.
Data and Ai Engineer (R & d Ai Product) @Sanofi
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WORK HISTORY
Data and Ai Engineer (R & d Ai Product) @Sanofi
Toronto, ON, CA
o Productionizing AI at Scale: Successfully transitioning high-fidelity AI models from research into enterprise-grade products by architecting robust backends with FastAPI and Pydantic, ensuring seamless integration for frontend and downstream consumers.o System Architecture & Data Modeling: Designing scalable relational data schemas in PostgreSQL using SQLAlchemy and orchestrating unstructured data storage in AWS S3 to support high-throughput R&D workflows.o Engineering Operational Excellence: Implementing end-to-end CI/CD pipelines and Docker containerization strategies to automate testing, building, and deployment, reducing \"time-to-production\" for AI features.o Full-Stack ML Integration: Developing high-performance CRUD modules and router architectures to manage the persistent state of AI inferences, bridging the gap between Data Science prototypes and production bedrock.o Cross-Functional Leadership: Collaborating with Data Science pods and Product teams to translate complex scientific requirements into technical ADRs (Architecture Decision Records) and scalable system designs.
EDUCATION
Allameh Tabatabaei Complex of Cultural and Educational Units (ATCCE)
High School Diploma
McMaster University
M.Sc. Candidate
IBM Skills Network
Professional Certification
Sharif University of Technology
Bachelor of Science - BS
McMaster University
Doctor of Philosophy - PhD
ABOUT SHAYAN MOUSAVI M.
I specialize in solving the \"Acceleration Paradox\"—the gap between cutting-edge AI research and robust, production-grade industrial application. As a Data & AI Engineer at Sanofi with a PhD and a background as a Lead AI Scientist, I architect the end-to-end platforms that move AI out of the laboratory and into the hands of users at enterprise scale.My work sits at the intersection of High-Level Research and Product-Oriented ML Infrastructure. Currently, I am focused on productionizing AI-native products for R&D, transforming sophisticated experimental logic into high-performance, scalable software bedrock. My goal is to build the engineering systems that allow AI-first drug discovery to move from a scientific possibility to a reliable industrial reality.Proven Track Record in Strategic AI: Previously, as Program Technical Lead for a ~$60M national initiative at the National Research Council Canada, I architected the \"Digital Fabric\"—an ecosystem of autonomous research systems including: • AutoMatOOr: An autonomous research orchestrator for self-driving laboratories. • RE.Inform.ED: An agentic RAG system for structured knowledge extraction. • SokeGraph: A foundational LLM-based knowledge graph framework.Core Expertise & Architectural Focus: Bridging R&D & Production: Expert at translating research logic (Agentic Workflows, Bayesian Optimization) into production services using FastAPI, Pydantic, and SQLAlchemy. Scalable ML Infrastructure: Designing robust data schemas and managing high-throughput data environments by integrating structured PostgreSQL with unstructured S3 storage. Operational Excellence (MLOps): Implementing enterprise deployment patterns using Docker and CI/CD to ensure reliability in mission-critical R&D products.From receiving the Best Invention of the Year (2025) award to leading technical teams across sectors, I thrive on building systems that think, learn, and scale.I am driven by the challenge of architecting the future of autonomous innovation. Let’s connect to discuss the intersection of AI system design, drug discovery, and the next generation of intelligent platforms.
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