• Define and own the architectural roadmap for Cerebras's enterprise, data center, and cloud network estate. • Co-author secure design and lifecycle management of high-performance data center networking — including spine/leaf, RDMA, and high-bandwidth fabrics supporting wafer-scale compute. • Build and maintain an AI-agent based analysis and review framework for network changes as well as self-improvement driven by observed network patterns and use cases. • Partner with Network Security to deliver segmented, zero-trust-aligned network designs across data center and corporate environments. • Set standards for resiliency, observability, and capacity planning across the global network • Mentor engineers across regions (US, Canada, Bangalore, and beyond), and serve as the senior technical voice on cross-functional network initiatives. • Stay ahead of emerging networking patterns relevant to AI infrastructure and translate them into actionable architecture decisions.

• Design and operate firewall, segmentation, and zero-trust controls across data center, corporate, and cloud (AWS) networks. • Build and maintain network security infrastructure as code — including firewall rules, policy automation, and CI/CD-driven deployment. • Lead network lifecycle management: design review, configuration baselines, change automation, and ongoing rule hygiene. • Build detection capabilities for network-based attacker behaviors and partner with the Detection & Response team on response playbooks. • Implement and operate network access controls including ZTNA, VPN, and remote access patterns. • Drive periodic firewall rule reviews, segmentation audits, and remediation campaigns to reduce risk and complexity. • Document network security architecture, controls, and operating procedures in clear runbooks.

• Define security architecture and build controls for AI platforms, training and inference workflows, model-serving systems, customer workloads, developer workflows, and agentic • Develop reusable AI and agent security patterns for identity, authorization, delegated authority, scoped tool access, MCPs, connectors, secrets, approvals, isolation, auditability, and • Design runtime controls that constrain execution, access, data exposure, model and tool interaction, and blast radius. • Build security capabilities as code using infrastructure as code, configuration as code, policy as code, GitOps, CI/CD, and automated validation. • Define secure development patterns for AI systems, agents, prompts, tools, models, policies, evaluations, releases, and rollback. • Automate security reviews, policy checks, evidence collection, control validation, and remediation • Instrument AI, agent, and platform activity with telemetry, traceability, policy decisions, audit logs, anomaly signals, and response workflows. • Lead hands-on security reviews and influence product, platform, infrastructure, and security architecture through practical design changes and reusable controls.

• Partner with platform, infrastructure, and hardware teams to embed security controls from physical hardware through runtime. • Design and implement security hardening across the Linux kernel, bootloader, firmware, and host OS layers of Cerebras compute platforms. • Drive secure boot, measured boot, and attestation strategies across our infrastructure, from the wafer-scale system to supporting host nodes. • Conduct deep security reviews of kernel modules, drivers, and low-level system components — identifying and remediating memory safety, privilege escalation, and isolation issues. • Develop kernel-level monitoring and telemetry (e.g., eBPF) to enable detection of low-level attacker behavior. • Stay ahead of emerging kernel CVEs, supply chain risks, and hardware-level threats — driving response and remediation across the fleet. • Document low-level security posture, threat models, and remediation playbooks in clear, accessible language.