Full Stack Engineer

Role overview

Engineering and integration

• 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 integrations.
• Work with .NET primarily and use Python for scripting and automation.
• Build and deploy cloud-native AI/ML solutions on AWS, Azure, or GCP, using containers such as Kubernetes and Docker.
• Work in existing CI/CD pipelines; contribute to integration testing, development, and architecture discussions.
• Diagnose issues in development, test, and production, including performance and monitoring; own work through production support.
• Document technical designs, data flows, changes, and support procedures.
• Remediate vulnerabilities and support application control assessments and data attestations.

Data and collaboration

• Build data pipelines and scripts to extract, transform, and validate data for fraud, AML, and finance applications.
• Analyze large datasets for fraud detection, AML monitoring, reporting, and data-quality issues; prepare datasets and integrate model outputs.
• Work with IT leadership, business stakeholders, contractors, and partner teams to deliver solutions and support adoption.
• Apply AI and emerging tools where useful for development productivity and automation.

Required qualifications

• 3+ years of software engineering experience with one or more of .NET, Java, or Python.
• 2+ 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.
• Ability to diagnose issues across development, test, and production using tools such as Splunk or Elastic.
• Ability to document technical designs and data flows and work with technical teams and business stakeholders.
• Experience remediating security vulnerabilities and supporting application control assessments and data attestations.
• Proficiency in data extraction, transformation, and validation scripting, SQL, and large datasets.

Preferred qualifications

• Azure is preferred; Docker and Kubernetes experience is a plus.
• Familiarity with audit, risk, or regulatory control processes is nice to have.
• Nice to have: AI agents, LLMs, prompt engineering, fine-tuning, RAG, vector databases, LangChain, AutoGen, CrewAI, function calling, and model or data pipelines.

Compensation and employment

• Employment Type: Full time. Pay Type: Salaried.