Full Stack Engineer

Role overview

Responsibilities

• Develop, test, and maintain backend services, APIs, and web services for business applications.
• Implement SSO, security, and Kafka or MQ messaging integrations.
• Work primarily with .NET and use Python for scripting and automation.
• Develop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP; use Kubernetes and Docker where applicable.
• Work within existing CI/CD pipelines and contribute to integration testing and architecture discussions.
• Diagnose issues across environments and support performance monitoring with tools such as Splunk or Elastic.
• Own components through production, including monitoring and support; 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 for fraud, AML, and finance needs and identify data quality issues.
• Prepare datasets, run experiments, and integrate machine learning model outputs into applications.
• Collaborate with IT leadership, business stakeholders, contractors, and partner teams; apply AI tools where useful.

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.
• Experience building and consuming APIs and working in 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 monitoring tools such as Splunk or Elastic.
• Ability to document technical designs and data flows and work with technical teams and business stakeholders.
• Experience remediating security vulnerabilities and supporting control assessments and data attestations.
• Proficiency with SQL and scripting for data extraction, transformation, and validation using large datasets.

Preferred experience

• Docker or Kubernetes experience is a plus; building CI/CD pipelines from scratch is not required.
• Audit, risk, or regulatory control familiarity is nice to have.
• Preferred AI/LLM experience includes autonomous agents, prompt engineering, fine-tuning, RAG, vector databases, and frameworks such as LangChain, AutoGen, or CrewAI.
• Experience with model and data orchestration pipelines, model management, or advanced Python for AI/ML work is preferred.

Benefits and culture

• The posting describes a total rewards program, flexible benefits, coaching, and opportunities for challenging collaborative work.

Workplace

• The posting lists an RBC office address in Vancouver.