Full Stack Engineer

Opportunity

Engineering and integration work

• 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.
• Use existing CI/CD pipelines; contribute to integration testing, development, and architecture discussions.
• Diagnose issues across 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.
• Collaborate with IT leadership, business stakeholders, contractors, and partner teams; apply AI tools where useful.

Data and AI work

• 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, run experiments, and integrate model outputs into applications to support machine learning and AI models.

Must-have qualifications

• At least 3 years of software engineering experience with one or more of .NET, Java, or Python.
• At least 2 years of integration experience, including working knowledge of SSO, security, and Kafka or MQ messaging.
• Experience building and consuming APIs and working within existing CI/CD pipelines.
• Experience deploying cloud-native applications on AWS, Azure, or GCP; Azure is preferred.
• Ability to diagnose issues across environments and document technical designs and data flows.
• Experience with vulnerability remediation and application control assessments; proficiency in data extraction, transformation, and validation using SQL and large datasets.
• Docker or Kubernetes is a plus; audit, risk, or regulatory control familiarity is nice to have.

Preferred AI experience

• Nice to have: autonomous AI agents, LLM concepts, prompt engineering, fine-tuning, RAG, and vector databases.
• Nice to have: LangChain, AutoGen, or CrewAI; API function calling; data orchestration and model management pipelines; advanced Python for AI/ML.

Employment details

• The posting states full-time employment, regular job type, salaried pay type, and 37.5 work hours per week.

Workplace and employer