Full Stack Engineer

Role overview

Engineering and delivery

• Develop, test, and maintain back-end services, APIs, and web services for fraud, AML, finance, and risk applications.
• Implement integration components, including SSO, security, and Kafka or MQ messaging.
• Work primarily with .NET, using Python for scripting and automation.
• Develop and deploy cloud-native AI/ML solutions on AWS, Azure, or GCP; use Kubernetes and Docker as applicable.
• Use existing CI/CD pipelines to build, test, and deploy changes; building pipelines from scratch is not expected.
• Contribute to integration testing, development, and architecture discussions.
• Diagnose and resolve development, test, and production issues, including performance and monitoring concerns.
• Own work from development through production, including monitoring and support.

Security, data, and collaboration

• Document technical designs, data flows, changes, and support procedures.
• Remediate security vulnerabilities and support application control assessments and data attestations.
• Work with IT leadership, business stakeholders, contractors, and partner teams to deliver solutions.
• 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, and reporting, and identify data quality issues.
• Prepare datasets, run experiments, and integrate model outputs into applications to support machine-learning and AI models.
• Apply AI and emerging tools when they add value, including for development productivity and automation.

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; working knowledge of SSO, security, and Kafka or MQ messaging.
• Experience building and consuming APIs.
• Understanding of CI/CD and experience working in existing pipelines.
• Experience developing and deploying cloud-native applications on AWS, Azure, or GCP, focusing on scalability, reliability, and efficiency.
• Working knowledge of cloud platforms; Azure is preferred. Docker or Kubernetes experience is a plus.
• Ability to diagnose development, test, and production issues using tools such as Splunk or Elastic.
• Ability to document technical designs and data flows and collaborate with technical teams and business stakeholders.
• Experience remediating vulnerabilities and supporting control assessments and data attestations; audit, risk, or regulatory-control familiarity is nice to have.
• Proficiency in data scripting, SQL, and working with large datasets.

Nice-to-have qualifications

• Experience building autonomous AI agent systems and working knowledge of LLMs, prompt engineering, fine-tuning, RAG, and vector databases.
• Hands-on experience with LangChain, AutoGen, or CrewAI, and API design with function calling.
• Experience with data orchestration and model-management pipelines.
• Deep Python expertise for AI/ML work; basic Python or scripting for data and automation is a must-have.

What RBC offers

• Total Rewards Program, including bonuses, flexible benefits, competitive compensation, commissions, and stock where applicable.
• Development support through coaching and management opportunities.
• Collaborative team, challenging work, and opportunities to make a lasting impact.

Work schedule

• The posting lists 37.5 work hours per week.

About RBC