Description :

Responsibilities :

- Lead a team of engineers to build and deploy AI-driven proactive defense 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, behavioral modeling, 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.