AML-AI / ML Modelling 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 Engineer, you will design, train and deploy machine learning models that detect money laundering and financial crime. This position is vital for shaping the core detection capability of the platform. This also involves execution of various AML initiatives, thereby strengthening the organization’s overall risk management.

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

• Model Development & Evaluation – Design, train, and assess ML models (NNs, GNNs, gradient‑boosted ensembles, unsupervised anomaly detectors) for money‑laundering detection across transaction, client, and network levels.
• Feature Engineering Pipeline – Build and optimise Spark/Delta Lake pipelines, ensuring feature consistency between training and production.
• Lifecycle Management – Track experiments, version models, and run champion‑challenger workflows with MLflow.
• Low‑Latency Deployment – Deploy and maintain inference services (e.g., BentoML) on the on‑prem Kubernetes platform.
• Calibration & Scoring – Calibrate models and map scores to probabilities to deliver reliable, decision‑ready risk scores.
• Explainability & Governance – Provide model explainability for audits, document design/assumptions/validation, and guarantee full reproducibility and auditability for regulatory compliance.
• Cross‑Functional Collaboration – Partner with data engineers, graph/LLM specialists, and compliance analysts to translate detection requirements into production‑ready models.

Technical & Behavioral Competencies

• Python engineering & secure code – Expert in object‑oriented, modular, and reusable Python code; packaging, logging, metrics, and tracing (ELK, Prometheus, Grafana, OpenTelemetry); security primitives (OAuth2/JWT) and data‑privacy compliance (GDPR/CCPA).
• ML modeling & calibration – Hands‑on with PyTorch (custom architectures, focal loss, training loops, GPU optimization), XGBoost/LightGBM, scikit‑learn/Keras/TensorFlow; deep knowledge of model calibration (Platt scaling, score‑to‑probability, MC‑Dropout), evaluation (PR/AUC‑ROC, F‑beta, lift charts) and handling class imbalance along with the underlying statistical concepts (calibration theory, hypothesis testing).
• Large‑scale data processing – Proficient in Pandas, Dask, Polars (or equivalent) for transactional‑scale datasets; feature engineering, distributed pipelines, and performance‑aware data handling.
• MLOps & lifecycle management – MLflow (experiment tracking, model registry, stage promotion), BentoML (runners, adaptive batching, CPU/GPU pools), GitOps/Argo CD for A/B‑testing and champion‑challenger workflows; CI/CD pipelines with Git/Bitbucket.
• Python microservices & containerization – Designing and deploying APIs with FastAPI, Flask, or Django; async I/O, Celery task queues, Docker packaging, and Kubernetes orchestration for scalable inference services.
• Data storage & query optimization – Strong command of SQL (PostgreSQL, Oracle) and NoSQL/graph stores (Cassandra, MongoDB, Neo4j, JanusGraph); schema des…