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 extraction (< 200 ms), and apply graph algorithms for risk scoring. - LLM & Retrieval‑Augmented‑Generation Proficiency: Hands‑on experience with LangChain/LangGraph, prompt engineering, multi‑agent design, and vector‑database ecosystems (Pinecone, pgvector, Weaviate). Capable of building robust RAG architectures, chunking, embedding, retrieval, re‑ranking and integrating LLM gateways with cost‑control and fallback mechanisms. - Backend & Integration Engineering: Solid track record building high‑availability micro‑services (FastAPI/Node.js), event‑driven pipelines (Kafka, Avro, Schema Registry), and rule‑engine integrations (Drools or custom Python). Skilled at designing state‑machines, de‑duplication engines, and API‑gateway (Kong) configurations that expose ML outputs to downstream investigators. - Cloud‑Native & Data‑Platform Ops: Deep familiarity with on‑prem Kubernetes (multi‑namespace, Helm, ArgoCD GitOps), CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), and observability stacks (Prometheus‑Grafana, ELK/Loki). Proficient in Spark/Delta Lake, Flink/Kafka Streams, Terraform/Pulumi, and Docker containerization, including GPU node provisioning and sandbox quota management. - Data‑Engineering Fundamentals & Governance: Adept at handling noisy, large‑volume data: incremental ETL, schema evolution, ACID compliance, and feature‑store patterns (Feast, Redis). Champions data‑quality, lineage, auditability, and regulatory compliance throughout the pipeline. - Collaboration & Communication Excels in globally distributed, cross‑functional teams; translates complex ML concepts into actionable requirements for compliance officers, investigators, and business stakeholders. Communicates clearly in written and oral forms, writes comprehensive documentation (OpenAPI/Swagger, design specs), and mentors junior engineers. - Problem‑Solving Mindset & Adaptability Approaches ambiguous AML typologies with curiosity and a systematic, data‑driven methodology. Quickly learns new financial‑crime domains, navigates regulated environments, and iterates on solutions under tight timelines while maintaining high quality and security standards.

Nice To Have Skills

• Experience with TigerGraph, Amazon Neptune, or JanusGraph for large‑scale network analytics.
• Familiarity with vLLM, LoRA/QLoRA fine‑tuning, and serving models on GPU clusters behind the firewall.
• Expertise building multi‑agent systems, tool‑calling patterns, and custom “Chain‑of‑Thought” prompt libraries.
• Deep knowledge of AML‑related standards (FATF, FinCEN, BSA, GDPR), SAR filing formats, and experience automating compliance documentation.

Education Level Bachelor Degree or equivalent Experience Level At least 7 years

Job Title AIML_Engineer

Date 18/05/2026

Department CEP IT Location: Mumbai Business Line Anti Money Laundering

Reports To AML-IT Manager

About BNP Paribas India Solutions Established in 2005, BNP Paribas India Solutions is a wholly owned subsidiary of BNP Paribas SA, European Union’s leading bank with an international reach. With delivery centers located in Bengaluru, Chennai, and Mumbai, we are a 24x7 global delivery center. India Solutions services three business lines: Corporate and Institutional Banking, Investment Solutions and Retail Banking for BNP Paribas across the Group. Driving innovation and growth, we are harnessing the potential of over 10000 employees, to provide support and develop best-in-class solutions.

About BNP Paribas Group BNP Paribas is the European Union’s leading bank and key player in international banking. It operates in 65 countries and has nearly 185,000 employees, including more than 145,000 in Europe. The Group has key positions in its three main fields of activity: Commercial, Personal Banking & Services for the Group’s commercial & personal banking and several specialized businesses including BNP Paribas Personal Finance and Arval; Investment & Protection Services for savings, investment, and protection solutions; and Corporate & Institutional Banking, focused on corporate and institutional clients. Based on its strong diversified and integrated model, the Group helps all its clients (individuals, community associations, entrepreneurs, SMEs, corporate and institutional clients) to realize their projects through solutions spanning financing, investment, savings and protection insurance. In Europe, BNP Paribas has four domestic markets: Belgium, France, Italy, and Luxembourg. The Group is rolling out its integrated commercial & personal banking model across several Mediterranean countries, Turkey, and Eastern Europe. As a key player in international banking, the Group has leading platforms and business lines in Europe, a strong presence in the Americas as well as a solid and fast-growing business in Asia-Pacific. BNP Paribas has implemented a Corporate Social Responsibility approach in all its activities, enabling it to contribute to the construction of a sustainable future, while ensuring the Group's performance and stability Commitment to Diversity and Inclusion At BNP Paribas, we passionately embrace diversity and are committed to fostering an inclusive workplace where all employees are valued, respected, and can bring their authentic selves to work. We prohibit Discrimination and Harassment of any kind, and our policies promote equal employment opportunity for all employees and applicants, irrespective of, but not limited to their gender, gender identity, sex, sexual orientation, ethnicity, race, color, national origin, age, religion, social status, mental or physical disabilities, veteran status etc. As a global Bank, we truly believe that inclusion and diversity of our teams is key to our success in serving our clients and the communities we operate in.