Job Description
The Opportunity:
We are building the digital backbone for the 2026 Los Angeles Olympics. As a Senior AI Architect, you will lead the deployment of cutting-edge artificial intelligence and machine learning systems designed to enhance fan experiences, optimize venue logistics, and ensure the smooth operation of the world's most anticipated sporting event.
Join a high-performance team dedicated to legacy and innovation. You will work on massive-scale data processing, predictive modeling for crowd management, and real-time decision support systems.
Why Join Us?
- Historic Impact: Shape the technological foundation for 8 million+ attendees.
- Elite Team: Collaborate with world-class engineers and data scientists.
- Innovation: Push the boundaries of AI in smart city infrastructure.
Responsibilities
- Design and architect scalable AI solutions for predictive analytics and real-time data processing.
- Develop machine learning models to optimize crowd flow, security protocols, and venue resource allocation.
- Lead the integration of AI-driven insights into the 2026 Games operational dashboard.
- Collaborate with cross-functional teams (Security, Logistics, Hospitality) to translate complex data into actionable intelligence.
- Mentor junior engineers and define best practices for AI deployment in high-stakes environments.
- Ensure data privacy, security, and ethical AI usage across all platforms.
Qualifications
- 7+ years of experience in software engineering, with at least 3 years in a Senior AI or Machine Learning Architect role.
- Proven expertise in Python, TensorFlow, PyTorch, and distributed computing frameworks (Kubernetes, Spark).
- Strong background in Natural Language Processing (NLP) and Computer Vision applications.
- Demonstrated experience deploying AI models in production environments handling high-throughput data.
- Excellent communication skills with the ability to translate technical concepts for non-technical stakeholders.
- Master’s degree in Computer Science, Engineering, or a related field is preferred.