Minimum Qualifications- Education & Prior Job Experience
- Bachelor’s degree in Data Science, Computer Science, Systems Engineering, or related field
- 5-8 years of leadership experience, including experience leading other technical leaders and team members
- 5-8 years of experience in leading Agile, DevOps and security practices to manage technology delivery
- 5-8 years of experience leading AI/ML teams, managing large-scale AI solutions, with a focus on natural language processing (NLP) techniques
- 5-8 years of experience collaborating with cross-functional architecture groups
- Strong understanding of cloud computing principles, including infrastructure for AI, microservices architecture, and micro models.
- Strong knowledge of AI architecture, cloud computing, and scalable AI infrastructure (e.g., AWS, Azure, GCP).
- Excellent verbal and written communication skills, with proven ability to communicate complex technical concepts to non-technical stakeholders, including executives and board members.
- Demonstrated experience working with cross-functional teams (e.g., engineering, product management, legal, ethics, data science) to deliver AI and/or platform engineering solutions.
Preferred Qualifications- Education & Prior Job Experience
• Advanced degree (Masters, Ph.D.) in Data Science, Computer Science, Systems Engineering, or related field
• Proficiency in programming languages such as Python, R, or Java.
• 2 years of experience working with Generative AI foundation models (e.g., GPT, Llama, Gemini)
• Demonstrated experience managing large, complex projects, including budgeting, timelines, and resource allocation
• Experience with AI-specific platform technologies (e.g., NVIDIA GPU Cloud, Lambda Labs, or other AI-optimized infrastructure).
• Experience developing and integrating APIs to support AI model access and deployment across applications.
• Proficiency with microservices architecture for scalable and modular AI services.
• Knowledge of infrastructure as code (e.g., Terraform, CloudFormation) for automating and scaling environments.
• Familiarity with continuous monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) to ensure AI platform reliability.
• Proven ability to build and facilitate relationships at all levels of the organization, as well as manage relationships with 3rd party providers and partners.