Overview

SonicWall is a cybersecurity forerunner with more than 30 years of expertise and is recognized as a leading partner-first company, ensuring our partners and their customers are never alone in the fight against cybercrime.

Responsibilities

• We are seeking a Distinguished Engineer - AI Architect to pioneer the intelligence layer of our next-generation security platform. In this role, you will be the ultimate technical authority on our AI strategy, defining how we use machine learning and large language models to reason over complex security telemetry, unstructured logs, and customer network configurations.
• As part of a lean Data & AI team, you will design the overarching AI runtime architecture, implement state-of-the-art reasoning frameworks, and establish the strict guardrails required to deliver safe, highly accurate, and deterministic AI capabilities within a mission-critical cybersecurity environment.
• Define the AI System Architecture: Architect the blueprint for our AI runtime, including agentic workflows, complex Retrieval-Augmented Generation (RAG) systems, and multi-modal reasoning engines optimized for complex technical data.
• Lead Modeling & Fine-Tuning Strategy: Set the technical direction for model selection, domain-specific fine-tuning (SLMs/LLMs), and embedding strategies tailored to interpret network behaviors, security schemas, and configuration states.
• Establish AI Trust, Alignment, & Safety: Design and implement pioneering evaluation frameworks and runtime guardrails. You will solve the critical challenge of making AI outputs safe, deterministic, and audit-ready for security operations.
• Co-Architect the AI Data Fabric: Partner closely with our Distinguished Data Engineer to design high-performance feature stores, semantic indexes, and low-latency vector retrieval layers that support real-time inference at scale.
• Drive R&D into Production: Keep the company at the cutting edge by evaluating and commercializing emerging AI paradigms (e.g., small specialized models, advanced reasoning LLMs, context-window optimization), translating research into production-ready software.

Qualifications

• Generative AI & LLM Mastery: Proven track record of architecting production-grade generative AI systems. Deep, authoritative expertise in model training, alignment (RLHF/DPO), prompt compilation, and orchestration frameworks.
• Engineering & MLOps Infrastructure: Deep understanding of distributed training and inference infrastructure, model optimization techniques (quantization, pruning, caching), and cloud ML infrastructure (AWS Vertex/SageMaker, Kubernetes, or custom GPU compute stacks).
• Cybersecurity DNA: A strong understanding of security concepts, log formats, or threat intelligence is highly desirable; you must possess the ability to rapidly master domain-specific security contexts.
• Mindset: You thrive on the autonomy of a lean team, love writing foundational code, and have the pragmatic engineering discipline to deliver high-business-value AI capabilities safely and quickly.