JAYDIP KULKARNI

Led development and enhancement of Integrated Fraud Management (IFM) using Actimize AIS, RCM, and ActOne, including building NAF (New Account Fraud) and EAM (Early Account Monitoring) products from scratch.

Delivered multiple patches and service packs with zero regression, while owning L4 production support and the complete security lifecycle for IFM releases.

Managed critical functional and performance escalations, delivering hotfixes within tight timelines for global clients (Citi, PNC, CIMB, KeyBank).

Drove secure SDLC practices, including threat modeling, managing SAST, DAST, and SCA scans, penetration testing, and client-raised vulnerabilities to ensure compliance with security gates.

Introduced a SonarQube-first validation framework to strengthen code quality and security compliance.

Developed PySpark-based data transformation pipelines for the Cortium project, enabling structured exposure of transactional normalized detection scores to the Actimize Watch team; leveraged AWS Glue (Glue APIs), PySpark, Amazon S3, and Amazon Aurora DB to process and persist fraud analytics data.

Spearheaded a multi-agent AI security automation project leveraging AWS Bedrock, designed to automate vulnerability remediation and compatibility checks before PR submission—reducing manual effort and accelerating release cycles.

Tech stack: Core Java, Scala, PySpark, Apache Spark, Actimize AIS/RCM/IFM, AWS (EC2, S3, EKS, Glue), Docker, Kubernetes, CI/CD, Cassandra.

Mentored engineers and led Agile delivery, sprint planning, estimations, and code/design reviews to foster collaboration across global teams.

Engineered containerized Actimize IFM solutions using Docker and Kubernetes, improving scalability and deployment security.
Strengthened CI/CD pipelines with integrated SAST, DAST, and SCA scans to ensure secure releases and compliance.
Collaborated with security teams to remediate vulnerabilities flagged by Veracode, Black Duck, and Invicti before production deployment.
Resolved Orca Cloud security findings for AWS environments, ensuring hardened container images and secure AMI configurations.
Led migration from DateTime to LocalDateTime across 200+ files, maintaining backward compatibility and zero regressions.

Key Projects:

Loan Probability Index Calculator: Built an index to forecast a company’s likelihood of securing a business loan, enabling actionable insights for improving creditworthiness.
Automated Document Processing: Designed a system to automate registration workflows by extracting entity data (Investors, Companies, Sponsors) from documents and validating against government records.

Responsibilities & Achievements:

Implemented data optimization techniques (indexing, partitioning, bucketing) to accelerate query performance on large historical datasets.
Developed real-time data ingestion pipelines using Flume for streaming and SQOOP for relational data extraction into HDFS/Hive.
Leveraged HBase for real-time storage and integrated reporting via Impala, enabling fast analytics and visualization.
Applied Machine Learning models (CART, Neural Networks) for predictive insights.
Technologies: Hadoop, HBase, Hive, Impala, Spark, Kafka, and ML frameworks.

• Process the split table-data which are highly voluminous with Map Reduce & Spark SQL
• Core-customization & Issue Analyzing & finding out the root-causes & giving the hot data-fixes if required