Full Stack Engineer
Role Overview
The Senior Full Stack Engineer works in AI Engineering within US Functions Technology, supporting finance, risk, law, and audit technology.
The role builds and supports back-end services, APIs, integrations, and data solutions for fraud, AML, finance, and risk applications.
Work includes integrating CNB US Functions technology into RBC enterprise platforms and supporting application modernization.
Key Skills for This Role
Full Job Posting
Role overview
The Senior Full Stack Engineer works in AI Engineering within US Functions Technology, supporting finance, risk, law, and audit technology.
The role builds and supports back-end services, APIs, integrations, and data solutions for fraud, AML, finance, and risk applications.
Work includes integrating CNB US Functions technology into RBC enterprise platforms and supporting application modernization.
Responsibilities
- Develop and maintain services, APIs, and web services; implement SSO, security, and Kafka or MQ integrations.
- Develop and deploy cloud-native AI/ML solutions and deliver changes through existing CI/CD pipelines.
- Contribute to integration testing and architecture discussions; diagnose issues across development, test, and production.
- Own work through deployment, monitoring, and support, and document designs, data flows, changes, and procedures.
- Remediate vulnerabilities and support application control assessments and data attestations.
- Build data pipelines and scripts, analyze large datasets, and support model data preparation and integration.
- Collaborate with technology, business, contractor, and partner teams; apply useful AI and emerging tools.
Required 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 SSO, security, and Kafka or MQ messaging.
- Experience building and consuming APIs, working with CI/CD, and deploying cloud-native applications on AWS, Azure, or GCP.
- Ability to troubleshoot across environments and use monitoring tools such as Splunk or Elastic.
- Experience documenting designs and data flows, remediating vulnerabilities, supporting control assessments, and validating data with SQL.
Preferred qualifications
- Azure is preferred; Docker and Kubernetes are a plus.
- Audit, risk, or regulatory controls familiarity is desirable.
- AI agent and LLM development, prompt engineering, fine-tuning, RAG, vector databases, LangChain, AutoGen, CrewAI, function calling, and data/model pipelines are preferred experience.
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