AML or Fraud domain Developer

3 days ago

Bengaluru, Karnataka, India InvestM Technology LLC Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Job Title:
AML/Fraud Domain Developer

Job Type:
Contract

Locations: Onsite

  • Bengaluru
  • Mumbai
  • Pune

Job Summary
We are seeking experienced and hands-on developers with strong expertise in the Anti-Money Laundering (AML) and Fraud domain. The ideal candidate will have practical experience working with platforms such as
SymphonyAI NetReveal
,
FircoSoft Continuity
,
Quantexa
,
Fenergo
,
Actimize (SAM, IFMx, Trade Surveillance)
, and Oracle
OFSAA FCCM
. This role is focused on development and integration, not business analysis or configuration.

Key Responsibilities

  • Develop and customize AML and fraud detection solutions using one or more of the following platforms: NetReveal, FircoSoft, Actimize, Quantexa, Fenergo, OFSAA FCCM.
  • Implement transaction monitoring, customer risk scoring, and case management workflows.
  • Integrate AML systems with upstream/downstream data sources and enterprise systems.
  • Optimize performance and scalability of AML solutions.
  • Collaborate with compliance, data, and infrastructure teams to ensure regulatory alignment.
  • Troubleshoot and resolve technical issues in AML/Fraud systems.

Required Skills & Experience

  • Hands-on development experience in at least one of the following platforms: SymphonyAI NetReveal, FircoSoft Continuity, Quantexa, Fenergo, Actimize (SAM, IFMx, Trade Surveillance), Oracle OFSAA FCCM
  • Strong programming skills in Java, PL/SQL, Unix Shell Scripting, or relevant technologies.
  • Experience with AML data models, batch processing, and alert/case workflows.
  • Familiarity with financial crime compliance regulations and standards.
  • Ability to work independently and deliver high-quality code in a fast-paced environment.

Preferred Qualifications

  • Experience in banking or financial services.
  • Exposure to regulatory reporting and compliance analytics.
  • Knowledge of KYC/CDD, BSA/AML, and fraud detection methodologies.

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