Full Stack Engineer

Role overview

Engineering and integration

• Develop, test, and maintain back-end services, APIs, and web services for Fraud, AML, Finance, and Risk applications.
• Implement SSO, security, and messaging integrations, including Kafka or MQ, following the integration plan.
• Work primarily with .NET, using Python for scripting and automation.
• Develop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP and technologies such as Kubernetes and Docker.
• Work within existing CI/CD pipelines to build, test, and deploy changes.
• Contribute to integration testing, development, and architecture discussions for assigned components.

Operations, security, and data

• Diagnose issues across development, test, and production, including performance and monitoring issues using tools such as Splunk or Elastic.
• Own work from development through production, including monitoring and support.
• Document technical designs, data flows, changes, and support procedures.
• Remediate security vulnerabilities and support application control assessments and data attestations.
• 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.
• Collaborate with IT leadership, business stakeholders, contractors, and partner teams; apply useful AI tools to productivity and automation.

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 working knowledge of SSO, security, and messaging such as Kafka or MQ.
• Experience building and consuming APIs; understanding of CI/CD and experience working within existing 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 and Kubernetes experience is a plus.
• Ability to diagnose issues using monitoring tools such as Splunk or Elastic and document technical designs and data flows.
• Experience remediating security vulnerabilities and supporting application control assessments and data attestations.
• Proficiency in data extraction, transformation, and validation scripting, SQL, and working with large datasets.
• Familiarity with audit, risk, or regulatory control processes is nice to have.

AI and LLM assets

• Nice-to-have experience includes autonomous AI agents, LLMs, prompt engineering, fine-tuning, RAG, vector databases, and frameworks such as LangChain, AutoGen, or CrewAI.
• Additional assets include function-calling API design, data orchestration and model-management pipelines, and advanced Python for AI/ML.

Benefits and opportunities

• RBC advertises a comprehensive total rewards program, including bonuses and flexible benefits, with competitive compensation and commissions or stock where applicable.
• The posting describes coaching and development, collaborative work, and opportunities to take on challenging assignments.