Full Stack Engineer

What is the opportunity?
As Senior Full Stack Engineer in AI Engineering within US Functions Technology (Finance, Risk, Law & Audit), you will be a hands-on contributor helping modernize how US corporate functions build and run their technology. You will work on back-end services, APIs, and integrations for applications in Fraud, AML, Finance, and Risk.A core part of the role is supporting the integration of CNB US Functions technology into RBC's enterprise platforms. You will help consolidate applications from CNB Finance and Audit technology onto the RBC common platform, working with technology teams so that business stakeholders can adopt new solutions smoothly.You will contribute across the delivery lifecycle, from development and integration testing through to production deployment. You will work within established CI/CD pipelines and help keep applications secure, compliant, and well maintained. This includes remediating security vulnerabilities and supporting application control assessments and data attestations as they arise.This role offers opportunities to apply AI and emerging technologies, while focusing on core reliable, well-integrated engineering and data solutions.What will you do?
Develop, test, and maintain back-end services, APIs, and web services for Fraud, AML, Finance, and Risk applicationsImplement integration components, including SSO, security, and messaging (Kafka or MQ), following the integration plan.Work primarily with .NET stack, however also leveraging Python for scripting and automation use casesDevelop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP, ensuring scalability, reliability, and efficiency through Kubernetes, Docker, etcWork within existing CI/CD pipelines to build, test, and deploy changes. You will not be expected to build CI/CD from scratchContribute to integration testing, development, and architecture discussions for your assigned componentsDiagnose and resolve issues in development, test, and production environments, including performance and monitoring with tools such as Splunk or ElasticTake ownership of your work from development through production, including monitoring and supportDocument technical designs, data flows, changes, and support proceduresRemediate security vulnerabilities and support application control assessments and data attestationWork with IT leadership, business stakeholders, contractors, and partner teams to deliver solutionsApply AI and emerging tools where they add value to your work, such as improving development productivity, and automationBuild data pipelines and scripts to extract, transform, and validate data for fraud, AML, and finance applications.Analyze large datasets to support fraud detection, AML monitoring, and reporting, and identify data quality issues.Support machine learning/AI models by preparing datasets, running experiments, and integrating model outputs into applicationsWhat do you need to succeed?
Must have:
3+ years of Software Engineering experience (with one or more of the following languages- .NET, Java, Python)2+ years of Integration experience, Working knowledge of integration components, including SSO, security, and messaging (Kafka or MQ)Well versed with API design and consumption: Experience building and consuming APIsUnderstanding of CI/CD practices and experience working within existing pipelines. Building pipelines from scratch is not requiredExperience developing and deploying cloud-native applications on AWS, Azure, or GCP, with a focus on scalability, reliability, and efficiency.Working knowledge of cloud platforms, with Azure preferred. Experience with containers (Docker, Kubernetes) is a plusAbility 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 to work effectively with technical teams and business stakeholdersExperience remediating security vulnerabilities and supporting application control assessments and data attestations. Famliarity with audit, risk, or regulatory control processes is nice to haveProficiency in scripting for data extraction, transformation, and validation, with SQL and experience working with large datasets.Nice to have:
AI and LLM development: Experience building autonomous AI agent systems, with working knowledge of LLMs (transformers, tokenization, embeddings), prompt engineering, fine-tuning, RAG, and vector databases.AI frameworks and API design: Hands-on experience with frameworks such as LangChain, AutoGen, or CrewAI, and experience designing APIs with function calling.Model and data pipelines: Experience with data orchestration and model management pipelines.Advanced Python: Deep Python expertise for AI and ML work. Basic Python/scripting for data and automation remains a must-haveWhat's in it for you?
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