What are the responsibilities and job description for the Big Data Engineer - AI/ML and Fraud Strategy position at Interon IT Solutions?
Senior Big Data Engineer AI/ML and Fraud Strategy
Location
We are looking for a Senior Big Data Engineer to support the future growth of the client's fraud prevention platform.
The client is expanding into new financial products, including a debit card offering. This role will help define the technology strategy, data architecture, and AI/ML capabilities needed to support new fraud risks and payment-related use cases.
Responsibilities
Location
- Hybrid: 3 days onsite
- Approved locations: Pennsylvania, North Carolina, Texas, or Arizona
- Contract-to-hire
- U.S. citizens and Green Card holders only
We are looking for a Senior Big Data Engineer to support the future growth of the client's fraud prevention platform.
The client is expanding into new financial products, including a debit card offering. This role will help define the technology strategy, data architecture, and AI/ML capabilities needed to support new fraud risks and payment-related use cases.
Responsibilities
- Define the technology roadmap for the fraud prevention platform.
- Design scalable big data and cloud solutions.
- Build data pipelines for transaction, customer, payment, and behavioral data.
- Support real-time and batch fraud detection.
- Apply AI and machine learning for fraud detection, risk scoring, and anomaly detection.
- Work with fraud, risk, product, engineering, and business teams.
- Support the launch of the new debit card product.
- Evaluate payment-processing and financial-partner integrations.
- Develop AWS-based data and analytics solutions.
- Recommend architecture and technology best practices.
- Create technical designs, roadmaps, and solution documentation.
- Provide technical leadership and guidance to engineering teams.
- Strong big data engineering or data architecture experience.
- Strong experience in banking, payments, fintech, or financial services.
- Experience with Python, SQL, PySpark, or similar technologies.
- Experience with AI and machine learning solutions.
- Strong AWS cloud experience.
- Experience with Spark, Databricks, Kafka, Hadoop, or similar platforms.
- Experience with real-time or streaming data processing.
- Knowledge of data lakes, data warehouses, and lakehouse architecture.
- Experience with APIs, microservices, and event-driven systems.
- Strong understanding of data quality, governance, security, and lineage.
- Strong communication and technical leadership skills.
- Fraud prevention or transaction-monitoring experience.
- Debit card, credit card, ACH, digital wallet, or payment-processing experience.
- Experience with AWS services such as S3, Glue, Lambda, Kinesis, SageMaker, Redshift, EMR, or Step Functions.
- Experience with MLOps, model monitoring, feature engineering, or model governance.
- Experience working with payment processors, banks, card networks, or fintech partners.