Cyber Threat Defense Senior AI/ML Engineer

Quick Facts

Senior individual contributor role focused on AI/ML for cyber threat defense, including prevention, detection, and response.

Description

Design, build, and deploy AI-powered capabilities for threat hunting, anomaly detection, and automated incident response using large-scale security telemetry. Build and operationalize custom ML models and LLM-based workflows, owning the end-to-end lifecycle from data preparation and feature engineering through training, evaluation, deployment, and monitoring. Drive responsible, ethical, and well-governed AI integration across security controls while partnering with security and leadership teams.

Role Responsibilities

• Engineer intelligent automation across deterministic/scripted automation, custom ML models, LLM workflows, and agentic AI systems.
• Develop ML/LLM systems for investigation support, detection engineering assistance, and knowledge retrieval.
• Select the appropriate technique (deterministic automation, classical ML, generative AI, or combinations) based on problem structure, data, and operational risk.
• Prototype and evaluate emerging AI technologies for cyber detection and response.
• Collaborate with offensive security teams on AI-enhanced red teaming and adversarial emulation.
• Contribute to architecture for scalable, governed AI integration across security controls.
• Promote responsible and ethical AI in security operations (bias mitigation, explainability, model governance).
• Advise senior leadership and mentor engineers and analysts on AI-driven cybersecurity initiatives.

Requirements

• 7+ years of hands-on machine learning engineering experience, including fine-tuning, evaluating, and deploying custom models in production.
• Deep ML fundamentals: model training and evaluation (loss functions, precision, recall, F1, calibration), feature engineering, embeddings, and real-world issues (class imbalance, label noise, model drift).
• Proficiency in Python and hands-on experience with ML frameworks such as PyTorch or scikit-learn, including evaluation harnesses and experiment tracking.
• Hands-on experience building LLM-powered applications and agentic AI systems (RAG, fine-tuning, tool use, orchestration) grounded in ML fundamentals.
• Experience delivering production systems at scale: data pipelines, deployment, MLOps, and automation.
• Experience with enterprise cloud AI platforms (Azure AI Foundry, Amazon Bedrock, Google Cloud Vertex AI) or equivalent open-source/self-hosted infrastructure.
• Working understanding of cybersecurity fundamentals and how AI enhances defensive operations.
• Familiarity with AI governance and model risk management (validation, explainability, responsible AI).
• Strong communication and presentation skills with the ability to explain complex concepts to executives.
• Bachelor’s degree in computer science or quantitative field (MS/PhD preferred) or equivalent applied experience.

Benefits

Not specified in the provided posting.