Lead AI/ML Engineer – Security AI & LLMs

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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