About the role
We are seeking a Manager - AI Proactive Defence to lead engineering efforts in developing agentic AI frameworks, proactive fraud detection systems, and intelligent automation pipelines that detect, predict, and mitigate emerging risks in real time. Your team will build data-intensive, AI-augmented, and autonomous defence systems that go beyond traditional detection, leveraging machine learning, large language models, and agent-based reasoning frameworks to create self-improving protection layers against fraud, abuse, and anomalous behaviour. Candidates from advertising, financial services, or cybersecurity domains are encouraged to apply.
Responsibilities:
- Lead a team of engineers to build and deploy AI-driven proactive defence systems focused on fraud, risk, and anomaly detection.
- Design and implement agentic frameworks leveraging LangChain, LangGraph, or similar architectures for reasoning, orchestration, and decision automation.
- Develop and maintain MLOps pipelines for scalable model training, versioning, and deployment across distributed systems.
- Architect automated workflows that integrate data ingestion, feature generation, and AI-driven response mechanisms.
- Partner with data science, threat intelligence, and platform engineering teams to translate detection logic into production-grade, low-latency systems.
- Apply LLMs and autonomous AI agents for dynamic pattern recognition, entity linking, and context-aware decisioning.
- Drive roadmap execution, mentor engineers, and uphold engineering best practices in automation, observability, and continuous improvement.
Requirements:
- 10+ years of experience in software engineering, machine learning, or data-intensive systems, with 2+ years in a leadership or technical management capacity.
- Strong background in fraud/risk analytics, behavioural modelling, or anomaly detection, preferably in fintech, ad tech, or cybersecurity.
- Experience developing or integrating agentic AI frameworks using LangChain, LangGraph, Semantic Kernel, or similar technologies.
- Expertise in Python or Go, with familiarity in PyTorch, TensorFlow, or Scikit-learn.
- Proficient in MLOps, data pipeline architecture, feature engineering, and real-time model inference.
- Strong understanding of AI automation, prompt orchestration, and evaluation metrics for LLM-based systems.
- Proven ability to mentor teams, define engineering standards, and lead complex cross-functional initiatives.