Product Manager - AI/ML AML Platform

BNP Paribas·Mumbai·Onsite

DirectorFintechAI

Experience

12+ years

Employment

Full Time

Work mode

Onsite

Level

Director

Location

Mumbai

Posted

3 days ago

About the role

Job Title: Product Manager: AI/ML AML Platform

Department: CEP IT

About Business line/Function: The AML IT function within BNP Paribas CIB’s CEFS-technology delivers an end-to-end IT platform supporting the bank’s AML program. It converts regulatory and compliance rules into scalable, secure IT solutions to detect, investigate, and report suspicious activity globally.

As the technology backbone, it processes transaction data into actionable AML insights, ensuring regulatory compliance, operational resilience, and innovation.

Position Purpose

This next-gen AML role strengthens BNPP Group’s defenses against financial crime by leveraging cutting-edge tech and innovation.

As a Product Manager, you’ll be responsible for the strategic direction of an AI/ML-powered AML platform. This involves defining and refining a multi-year vision and a detailed quarterly roadmap, ensuring all developments are synchronized with business objectives, regulatory mandates, and quantifiable compliance targets.

What you'll do

Direct
Own the product vision & roadmap: define, communicate, and continuously evolve the vision and roadmap for an AI/ML-driven AML platform, aligning business strategy, regulatory obligations, and measurable client and compliance outcomes.
Drive product strategy & market alignment: assess market trends, competitor offerings, and emerging AI capabilities to shape product positioning; build business cases and secure sponsorship for new investments.
Manage stakeholders: act as the voice of the product to senior Compliance, Ops, Technology, and Risk leadership; negotiate trade-offs, manage dependencies across teams and vendors, and secure funding through compelling business cases.
Lead discovery & engagement: run discovery workshops, user journey mapping, and UX research with Compliance, Operations, and investigator users; translate insights into use cases, requirements, and prioritized product capabilities.
Drive GenAI innovation: identify and validate opportunities to apply LLMs and agentic AI with appropriate guardrails, human-in-the-loop design, and hallucination/risk controls.
Shape AI/ML capabilities: partner with relevant expert teams to define, prioritize, and productize ML use cases, making informed calls on model selection trade-offs, rules-vs-model-vs-hybrid approaches, and detection effectiveness vs. explainability vs. operational cost.
Build AML domain depth: partner with Compliance SMEs to interpret regulatory expectations and translate them into product capabilities, rapidly developing financial-crime expertise on the job.
Lead go-to-market & adoption: plan launches end-to-end with various stakeholder teams; develop enablement materials, training, release communications, and post-go-live success measures to drive adoption and demonstrable business value.
Guide Agile delivery: work with Product Owners, BAs, and dev teams to translate the roadmap into epics and prioritized backlogs, steering sprint outcomes, release scope, and sequencing without losing sight of strategic goals.
Measure & optimize outcomes: define and track product KPIs (alert quality, false-positive rates, investigator productivity, adoption, client satisfaction, model performance) and use them to steer roadmap and investment decisions.
Mentor & build capability: coach Business Analysts, POs, and delivery teams on AI/ML product practices, Agile/BDD methods, and regulatory trends to build a high-performing product organization.

Nice to have

AML/FinCrime domain knowledge—AML typologies, transaction monitoring, sanctions/PEP screening, KYC/CDD, case management, and regulatory frameworks (FATF, EU AMLD, OFAC).
Exposure to AI/model governance—model risk management, explainability techniques (SHAP/LIME), AI regulation (SR 11-7, EU AI Act), and responsible-AI practices.
Experience with AML platforms—Actimize, Quantexa, Feedzai, Oracle FCCM, or in-house detection engines.
Model performance & evaluation literacy: comfort with metrics such as precision-recall, ROC-AUC, false-positive rates, drift, and calibration, and experience turning them into product KPIs and roadmap decisions.
Knowledge of BPMN/workflow tools (e.g., Camunda) and experimentation/A-B testing platforms.
Behavioral Skills
Cross functional collaboration & Clear communication
Ability to synthetize / simplify