Lead AI/ML Engineer – Security AI & LLMs

For more than 30 years, SonicWall has delivered a partner-first unified cybersecurity portfolio that helps SMBs, MSPs, and IT teams consolidate network security, endpoint security, security service edge (SSE), and threat response across hybrid environments. By bringing connected technology, real-time threat intelligence, automation, and expert services together, SonicWall helps organizations simplify security operations, achieve better security outcomes, and lower total cost of ownership. Backed by a global ecosystem of more than 17,000 partners, SonicWall delivers enterprise-grade protection built for SMB and mid-market realities.

Role Overview

As our lead AI/ML Engineer, you will design, build, and scale the intelligence layer powering our next-generation security products. You will turn complex datasets—including configurations, security alerts, and raw network logs—into production-ready AI capabilities that drive automated analysis, reasoning, and intelligent recommendations.

Key Responsibilities

• Architect AI Systems: Design and deploy robust Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and LLM applications tailored to parse and reason over security telemetry and unstructured logs.
• Model Development & Tuning: Train, fine-tune, and evaluate ML/DL models for pattern matching, anomaly detection, and classification across massive, heterogeneous security datasets.
• Ensure AI Trust & Alignment: Implement strict guardrails, evaluation frameworks, and safety alignment techniques to ensure all model outputs and recommendations are deterministic, safe, and highly accurate.
• Establish MLOps: Build and maintain scalable ML pipelines (from data preprocessing to model monitoring in production), collaborating closely with Data and Product Engineers to ensure low-latency, secure inferences.

Required Experience & Skills

• Experience: 6+ years of professional experience deploying machine learning models into production, with 1+ years focused on LLMs and generative AI. Experience in the cybersecurity domain is highly valued.
• AI/NLP Stack: Deep expertise in Python, PyTorch/TensorFlow, and frameworks like LangChain, LlamaIndex, or Hugging Face.
• Data & Vector Systems: Hands-on experience with vector databases (e.g., Pinecone, Qdrant, Milvus) and semantic search strategies over complex, technical datasets.
• Production Engineering: Solid understanding of API design, containerization (Docker/Kubernetes), and cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).
• Mindset: A builder's mentality—comfortable setting technical direction, navigating ambiguity, and executing fast in a small team.

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