Principal AI Engineering Architect

📍 United States

USD 224,000-308,000 per year

SENIOR

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 AWS @ 3 Docker @ 3 KubeFlow @ 3 Kubernetes @ 3 MLOps @ 3 Machine Learning @ 4 Performance Monitoring Python @ 3 Security @ 4

Define and execute the technical roadmap for AI engineering, focusing on bringing the most recent advances in machine learning technology to help our customers efficiently discover and defeat threats.
Represent AI Engineering as a key contributor to the Architecture Board defining the overall architecture strategy for the SentinelOne Platform and Services.
Understand and define strategies to support cross domain requirements to use AI Platform and Services across SentinelOne.
Serve as a lead individual contributor who also mentors across distributed teams, fostering a culture of technical excellence and continuous innovation.
Design and build net-new AI capabilities to be used across SentinelOne's industry-leading cybersecurity platform.
Drive cross-functional collaboration across multiple engineering teams to ensure seamless integration and innovation in AI-powered cybersecurity products.
Oversee the establishment of best practices for MLOps, security, and performance monitoring to ensure the reliability and efficacy of our AI defense mechanisms.

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), cloud-native MLOps platforms (e.g., Kubeflow), and large-scale data processing technologies, including hands-on familiarity with Docker, Kubernetes, and operating in cloud environments, especially AWS and Google Cloud.