Arctic Wolf is seeking a Manager, Engineering (Artificial Intelligence Proactive Defense Systems) to lead the design and development of AI‑driven proactive defense systems focused on fraud detection and analytics, risk analysis, behavioral modeling, and anomaly detection. In this leadership role, you will guide a team of engineers to build intelligent, automated systems that deliver faster and more accurate detection and response to emerging cyber threats, helping Arctic Wolf execute on its mission to end cyber risk.

You will be responsible for architecting and delivering agent‑based frameworks for reasoning, orchestration, and automated decision‑making, as well as building and maintaining machine learning operations pipelines for scalable model training, versioning, and deployment across distributed systems. The role includes designing automated workflows that integrate data ingestion, feature generation, and intelligent response mechanisms, partnering closely with data science, threat intelligence, and platform engineering teams to translate detection logic into production‑ready, low‑latency systems.

The manager will apply large language models and autonomous AI agents for pattern recognition, entity linking, and context‑aware decision‑making, while driving execution of the engineering roadmap and ensuring alignment with organizational goals. You will mentor and develop engineers, foster a culture of technical excellence and accountability, and establish best practices in automation, observability, and continuous improvement across the proactive defense stack.

You will be successful in this role if you bring more than 10 years of experience in software engineering, machine learning, or data‑intensive systems, including at least 2 years in a leadership or technical management role, along with a strong background in fraud detection and analytics, risk analysis, behavioral modeling, or anomaly detection. You should have deep experience with AI/ML technologies, strong programming skills in Python or Go, familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit‑learn, and solid knowledge of MLOps, data pipeline architecture, feature engineering, and real‑time inference, plus proven ability to mentor teams and lead cross‑functional initiatives. Experience with financial technology, ad tech, or cybersecurity domains, modern cloud and distributed systems, and building high‑scale, low‑latency production systems is considered a plus.