Full Stack Engineer

Role overview

Responsibilities

• Develop and maintain back-end services, APIs, and web services for Fraud, AML, Finance, and Risk applications.
• Implement integrations using SSO, security, and Kafka or MQ messaging; contribute to integration plans and architecture discussions.
• Work primarily with .NET and use Python for scripting and automation; develop cloud-native AI/ML solutions on AWS, Azure, or GCP.
• Build, test, and deploy changes through existing CI/CD pipelines; contribute to integration testing and production support.
• Diagnose development, test, and production issues; monitor performance and support applications through production.
• Document technical designs, data flows, changes, and support procedures; remediate vulnerabilities and assist with control assessments and data attestations.
• Build data pipelines and scripts; prepare, transform, and validate data and support datasets, experiments, and model integration.

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 components.
• 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.
• Ability to diagnose issues across development, test, and production using tools such as Splunk or Elastic.
• Ability to document 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 SQL and scripting for data extraction, transformation, and validation; experience with large datasets.

Preferred skills

• Docker and Kubernetes experience is a plus; familiarity with audit, risk, or regulatory control processes is nice to have.
• Nice-to-have AI experience includes autonomous agents, LLMs, prompt engineering, fine-tuning, RAG, vector databases, and LangChain, AutoGen, or CrewAI.
• Experience with data orchestration and model management pipelines and deeper Python expertise for AI/ML work is nice to have.

Benefits

• The employer describes a total rewards program, bonuses and flexible benefits, development and coaching opportunities, and collaborative, challenging work.

Schedule

• The posting lists 37.5 work hours per week.