Principal AI Engineering Architect
$224,000–$308,000 year
RemoteUnited States
Job Summary
Define and execute the technical roadmap for AI engineering, translating cutting-edge machine learning research into production-grade, globally scalable cybersecurity products. Represent AI Engineering on the Architecture Board to define overall platform strategy and support cross-domain requirements for AI services. Design and build net-new AI capabilities while overseeing MLOps, security, and performance monitoring best practices. Drive cross-functional collaboration across multiple engineering teams to ensure seamless integration and innovation in AI-powered solutions. Mentor distributed teams to foster technical excellence and continuous innovation.
Required Qualifications
- 15 or more years of progressive experience in software or machine learning engineering
- Proven track record of delivering multiple high-scale, production-ready AI or data products from concept through deployment and maintenance
- Deep expertise in building highly available, low-latency machine learning systems, including experience with machine learning workflows and processes
- Experience in endpoint security, OS concepts, or a related field with comparable large-scale event processing and real-time processing challenges
- Demonstrated ability to mentor, develop, and retain top-tier AI engineering talent
- Expert-level knowledge of Python and modern frameworks (e.g., Pydantic)
- Expert-level knowledge of cloud-native MLOps platforms (e.g., Kubeflow)
- Expert-level knowledge of large-scale data processing technologies
- Hands-on familiarity with Docker
- Hands-on familiarity with Kubernetes
- Hands-on familiarity with operating in cloud environments, especially AWS and Google Cloud
- Must be able to effectively lead and coach others
- Must be able to effectively lead and coach others
Desired Qualifications
- Expert-level knowledge of Python and modern frameworks (e.g., Pydantic)
- Expert-level knowledge of cloud-native MLOps platforms (e.g., Kubeflow)
- Expert-level knowledge of large-scale data processing technologies
- Hands-on familiarity with Docker
- Hands-on familiarity with Kubernetes
- Hands-on familiarity with operating in cloud environments, especially AWS and Google Cloud
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.