Full Stack Engineer

Role overview

Responsibilities

• Develop, test, and maintain back-end services, APIs, and web services for Fraud, AML, Finance, and Risk applications.
• Implement SSO, security, and Kafka or MQ messaging integration components.
• Use .NET primarily and Python for scripting and automation; contribute to integration testing and architecture discussions.
• Develop and deploy cloud-native AI/ML solutions on AWS, Azure, or GCP, using Kubernetes and Docker where applicable.
• Work within CI/CD pipelines to build, test, and deploy changes; diagnose and resolve issues across development, test, and production.
• Own work through production, including monitoring and support; document designs, data flows, changes, and support procedures.
• Remediate security vulnerabilities and support application control assessments and data attestations.
• Build data pipelines and scripts to extract, transform, and validate data; analyze datasets for fraud detection, AML monitoring, reporting, and data quality.
• Support ML/AI models by preparing datasets, running experiments, and integrating model outputs into applications.
• Collaborate with IT leaders, business stakeholders, contractors, and partner teams to deliver solutions.

Requirements

• At least three years of software engineering experience with .NET, Java, or Python.
• At least two years of integration experience, including SSO, security, and Kafka or MQ messaging.
• Experience building and consuming APIs and working within existing CI/CD pipelines.
• Experience developing and deploying cloud-native applications on AWS, Azure, or GCP; Azure is preferred.
• Docker and Kubernetes experience is a plus.
• Able to diagnose issues across development, test, and production; experience with monitoring tools such as Splunk or Elastic.
• Able to document technical designs and data flows and work effectively with technical teams and business stakeholders.
• Experience remediating security vulnerabilities and supporting application control assessments and data attestations; audit, risk, or regulatory controls are nice to have.
• Proficiency in SQL and scripting for data extraction, transformation, and validation, with experience working with large datasets.
• AI/LLM development, RAG, vector databases, frameworks such as LangChain, AutoGen, or CrewAI, and model/data pipelines are nice to have.

Benefits

• Total Rewards Program includes bonuses and flexible benefits, competitive compensation, and commissions and stock where applicable.
• Coaching and development opportunities, collaborative work, and challenging assignments.

Role details