7–12 years of software/data engineering experience with strong recent experience in Applied AI/ML.
Advanced Python and strong production software-engineering practices.
Strong ML knowledge including classification, regression, anomaly detection and gradient-boosting techniques.
Experience with explainable AI techniques such as SHAP/feature attribution.
Hands-on experience with LLMs, structured outputs, tool/function calling and prompt engineering.
Experience building agentic workflows using LangGraph, Semantic Kernel, LlamaIndex or similar frameworks.
Strong understanding of RAG, embeddings, vector/hybrid search and retrieval evaluation.
Strong SQL and data engineering skills; experience with APIs and event/streaming architectures such as Kafka.
Experience deploying AI/ML workloads on AWS or Azure.
Understanding of MLOps/LLMOps, model monitoring, evaluation, observability and CI/CD.
Highly Desirable
Experience within banking, capital markets, payments, financial crime, risk, reconciliation, regulatory reporting or other regulated transactional environments would be highly beneficial
