Principal AI and Data Architect

Zscaler Red Canary

3h•Remote

About The Position

We are looking for a Principal AI and Data Architect to join our Red Canary team. This is a Remote role, reporting to the VP, Software Engineering. You will architect the large-scale data infrastructure and advanced AI/ML frameworks necessary to automate threat detection and enhance SOC efficiency. Ultimately, this leader will transform massive security telemetry into actionable intelligence using ML Models, LLMs, and AI agents to drastically reduce manual analyst workloads.

Requirements

12+ years of experience inclusive of data architecture and AI/ML, with a track record of designing and implementing large-scale data systems and production-grade ML solutions
Proficiency in Python (for data processing and ML pipelines), cloud platforms (AWS and/or Azure including data storage, compute services, and security controls), and MLOps best practices
Bachelor's degree in Computer Science or a related field or equivalent

Nice To Haves

Security domain knowledge including SOC operations, threat detection methodologies, log formats, common attack techniques, and understanding of how ML can enhance detection and response
Familiarity with modern data pipelines, streaming technologies, and AI Agent frameworks
Proficiency with infrastructure-as-code, containerization, and CI/CD for data and model deployments

Responsibilities

Lead the design and implementation of large-scale data architectures for cloud-based systems (AWS, Azure) to efficiently ingest, store, and process massive volumes of security telemetry and alerts
Spearhead advanced AI/ML initiatives, including Generative AI, to develop end-to-end AI solutions for SOC automation, threat detection, and threat hunting, leveraging frameworks like Scikit-learn, TensorFlow, and PyTorch
Drive the use of Large Language Models (LLMs) and AI Agents to enhance the enrichment of security data, enabling faster human decision-making, while exploring and evaluating various LLM architectures
Collaborate across teams to integrate ML-driven insights into the platform and apply automation and analytics to reduce analyst workload and enhance detection fidelity
Provide architectural guidance across engineering based on the fast-paced world of GenAI, Agents, and classic ML models, including those developed by our internal R&D teams