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 integrations including SSO, security, and messaging such as Kafka or MQ.
• Work primarily with .NET and use Python for scripting and automation.
• Develop and deploy cloud-native AI/ML solutions on AWS, Azure, or GCP, using containers and Kubernetes as applicable.
• Work within existing CI/CD pipelines; contribute to integration testing, development, and architecture discussions.
• Diagnose development, test, and production issues, including performance and monitoring concerns.
• Own work through production, monitoring, and support; document designs, data flows, changes, and support procedures.
• Remediate security vulnerabilities and support application control assessments and data attestation.
• Collaborate with IT leadership, business stakeholders, contractors, and partner teams.
• Build data pipelines, analyze large datasets, identify data quality issues, and support ML/AI model development and 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 messaging such as Kafka or MQ.
• Experience building and consuming APIs and working within existing CI/CD pipelines.
• Experience deploying cloud-native applications on AWS, Azure, or GCP, with focus on scalability, reliability, and efficiency.
• Working knowledge of cloud platforms; Azure is preferred. Docker or Kubernetes experience is a plus.
• 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 vulnerabilities and supporting application control assessments and data attestations.
• Proficiency in data scripting and SQL, including extraction, transformation, validation, and working with large datasets.

Preferred experience

• Familiarity with audit, risk, or regulatory control processes is nice to have.
• AI and LLM development experience is nice to have, including agent systems, LLM concepts, prompt engineering, fine-tuning, RAG, and vector databases.
• Experience with LangChain, AutoGen, CrewAI, function-calling API design, data orchestration, or model management pipelines is a plus.
• Deep Python expertise for AI/ML is a plus; basic Python scripting for data and automation is required.

Employment details

• Employment type is full time; the posting describes the role as salaried but does not specify a salary cadence or amount.