AML-AI / ML Platform Engineer

About Business Line/Function The AML IT function sits within the BNP Paribas Corporate & Institutional Banking (CIB) CEFS‑technology ecosystem and provides end‑to‑end technology platform that enables the bank’s anti‑money‑laundering (AML) program. Its core purpose is to translate regulatory and compliance requirements into robust, scalable, and secure IT solutions that detect, investigate, and report suspicious activity across the Group’s global operations.

The AML IT business line is the technology backbone that transforms raw transaction data into actionable AML intelligence, ensuring that BNP Paribas CIB meets its regulatory obligations while maintaining operational resilience and innovation.

Position Purpose

The role centers on creating next‑generation AML capabilities and will be instrumental in protecting the BNPP Group from fraud, money‑laundering, and other financial crimes by using advanced technologies and innovative solutions.

As an AI-ML Platform Engineer, you will be responsible for turning advanced machine‑learning research into production‑grade solutions that detect fraud, money‑laundering and other financial‑crime typologies across the entire transaction‑monitoring value chain. This position is vital for shaping the core detection capability of the platform. You would be the technical cornerstone linking graph analytics, LLM‑driven reasoning, ML model outputs, reliable backend services and cloud‑native infrastructure to deliver a unified next‑generation AML detection platform.

While direct AML experience is not mandatory, a strong foundation in machine learning, sound engineering practices, and a willingness to learn the financial-crime domain are essential. Familiarity with regulated environments, model governance, or fraud and anomaly detection is an advantage, but we are primarily looking for engineers who can build robust, well-calibrated, production-grade models and reason carefully about their real-world impact. The role will operate within a globally distributed environment.

Direct Responsibilities

• Build and maintain a large‑scale knowledge graph (entities, transactions, ownership, sanctions, registries). Design ETL pipelines, entity‑resolution logic, and network‑level feature jobs for GNN models that score shell companies, detect circular flows, assess sanctions proximity and community risk.
• Create end‑to‑end pipelines that convert regulatory/investigative text into vector embeddings, store them in a vector DB, and orchestrate LLM‑based narrative generation, multi‑agent reasoning, and on‑demand query answering. Manage LLM gateways, prompt engineering, cost tracking and fallback handling for uncertain alerts.
• Own the non‑ML micro‑services that make ML outputs actionable (Alert Service, Case Manager, API Gateway, tuning matrix, deduplication, Signal Aggregator, rule‑engine integration, feature‑factory APIs, model registry). Ensure high‑throughput, low‑latency, event‑driven processing with Kafka, PostgreSQL, Redis and Docker deployments.
• Apply rigorous software‑engineering practices (CI/CD, MLflow, automated testing, monitoring, model governance, audit logging) to keep models robust, calibrated, explainable and compliant.
• Manage the on‑prem Kubernetes ecosystem (multi‑namespace clusters, Helm, ArgoCD GitOps, resource quotas, GPU scheduling) and the full data‑processing stack: Apache Spark/PySpark (batch & streaming), Flink/Kafka Streams, dbt incremental models, Terraform/Pulumi IaC, and observability (Prometheus‑Grafana, ELK). Handle node provisioning, container images (Docker, ECR/Harbor) and end‑to‑end logging/monitoring/alerting.
• Collaborate with distributed data‑science, compliance, investigation and platform teams to translate domain expertise into scalable, production‑ready AML solutions that continuously improve BNPP’s risk‑management posture.

Technical & Behavioral Competencies- Advanced Machine‑Learning Engineering: Design, train, and ship production‑grade models (GNNs, LLMs, RAG pipelines) that are well‑calibrated, explainable and meet strict AML governance requirements. Demonstrates a deep understanding of model lifecycle management, performance monitoring, and continuous improvement. Graph‑Data Mastery: Expertise in knowledge‑graph modelling (Neo4j/Cypher or similar), graph‑ETL at scale, and graph‑neural‑network frameworks (PyG, DGL). Able to engineer high‑throughput network‑feature pipelines, optimize sub‑graph extrac…