Associate Director, AI Engineering – Live Operations

• use technical expertise in data science and real-time AI systems to lead a team focused on the production lifecycle, health, and optimization of deployed models
• ensure commercial AI applications remain highly accurate, safe, and operationally efficient
• monitor and observe real-world model performance to proactively detect model drift, track latency, and manage computational expenses
• design and implement continuous evaluation loops to assess model accuracy on a day-to-day basis
• manage incident response protocols and automated safety guardrails
• establish fallback architectures to ensure a seamless experience if a model fails
• automate continuous deployment (CI/CD) pipelines
• operate as a technical thought leader and trusted advisor to partner with broader data science teams and senior leadership

• Bachelor’s degree or four or more years of work experience
• Eight or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training
• Direct experience managing teams of data scientists or research-oriented talent in a production, operations, or Live Ops capacity
• In-depth working experience with deep learning frameworks (such as PyTorch), Generative AI, and distributed computing systems
• Experience building and maintaining automated model monitoring, evaluation, and CI/CD pipelines in a cloud or edge production environment
• Proficiency in developing real-time safety filters, system guardrails, and automated fallback systems for conversational or predictive AI
• Coding proficiency in Python, Java, or R, alongside experience with AWS, Hadoop, or other distributed compute platforms

Demonstrates expertise in managing data science teams and optimizing AI model performance through continuous evaluation and automated deployment processes. Proficient in developing safety protocols and fallback architectures to ensure operational efficiency in AI applications.

• Team Management
• Deep Learning Frameworks
• Automated Model Monitoring
• CI/CD Pipeline Development
• Real-Time AI Systems

• Data Science
• Model Optimization
• Deep Learning
• Python
• Java
• R
• Generative AI
• Automated Safety Filters
• Model Drift Detection
• Cloud Computing

• Technical Leadership
• Collaboration
• Problem-Solving
• Communication

• Production Lifecycle
• Operational Efficiency
• Incident Response Protocols
• Fallback Architectures
• Live Ops

• AWS
• Hadoop
• Distributed Computing Systems
• CI/CD Tools
• Real-Time Monitoring Tools