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 integrations.
• Work primarily with .NET and use Python for scripting and automation.
• Deploy cloud-native AI/ML solutions using AWS, Azure, or GCP and technologies such as Kubernetes and Docker.
• Work within existing CI/CD pipelines; contribute to integration testing, development, and architecture discussions.
• Diagnose and resolve issues across development, test, and production, including performance and monitoring issues.
• Own work through production, monitoring, and support; document designs, data flows, changes, and procedures.
• Remediate 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.
• Prepare datasets, run experiments, and integrate model outputs; apply AI tools where useful.

Required qualifications

• 3+ years of software engineering experience with one or more of .NET, Java, or Python.
• 2+ years of integration experience and working knowledge of 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 and document technical designs and data flows.
• Experience remediating security vulnerabilities and supporting application controls and data attestations.
• Proficiency in data scripting and SQL, including data extraction, transformation, validation, and large datasets.

Preferred experience

• Azure is preferred; Docker and Kubernetes experience is a plus.
• Familiarity with audit, risk, or regulatory control processes is nice to have.
• AI agent systems, LLMs, prompt engineering, fine-tuning, RAG, vector databases, AI frameworks, and model or data pipelines are nice to have.
• Advanced Python for AI and ML is nice to have; basic Python scripting for data and automation is required.

Benefits and culture

• Total Rewards Program with bonuses, flexible benefits, competitive compensation, commissions, and stock where applicable.
• Coaching and development opportunities, meaningful work, and a collaborative team.