Tamir Larizki

Revamped client integration and sync processes using Python, Kafka, PostgreSQL, Neo4J, and KEDA, significantly enhancing reliability and scalability while decreasing client escalations by 89% and onboarding time by 80%. Additionally, engineered a cost calculation system with Python, PostgreSQL, and AWS, leading to 43% more accurate pricing and higher gross margin, and developed RBAC & ABAC for int

Led blockchain LST infrastructure and tools development using Golang, managing over $20M in assets at peak. Created analytics dashboards and tools using Python, Docker, PostgreSQL, and FastAPI, improving node runner performance by 27% and increasing user engagement by 65% on the website.

Developed a cutting-edge tool for back-testing options trading strategies using Django and Python, which was acquired by Bloomberg. Created complex data pipelines with Python, CICD tools, and Docker to handle over 20 years of options data, and researched and implemented trading strategies using Python and machine learning techniques, leading to new high-yield funds.

Spearheaded an autonomous Forex trading system using Java and Spring, trading on over 17,000 strategies with minimal latency, and implemented NVIDIA's Random Forest on GPU via CUDA and Cython, resulting in a 50x performance increase. Engineered a scalable infrastructure from scratch using Kubernetes, Docker, and CICD, efficiently handling thousands of microservices with near 100% availability, and

Completed a 3-year program focused on computer science. Acquired foundational knowledge in programming, algorithms, and data structures which laid the groundwork for subsequent professional and self-guided learning endeavors.