Lead, Full Stack Engineer

Job Description
What is the opportunity?
The Lead, Full Stack Engineer is a hands-on leadership role within AI Engineering, US Functions Technology - Finance, Risk, Law, and Audit. You will build and inspire a team that delivers scalable, production-ready solutions. You will lead modernising .NET applications, bring AI and agentic tools into everyday engineering, and drive CNB US Functions technology into the RBC Enterprise ecosystem, owning every modernization solution from concept to production.You will work at the core of data, APIs, and web services, delivering secure, high-impact integration across SSO, security, and messaging through Kafka or MQ. You will drive and lead the integration of CNB entities, working with teams to ensure seamless business adoption. You will also apply cutting-edge technologies to solve real-world problems across US Corporate Functions, including Finance, Risk, Law, and Audit.What will you do?
Innovate at the forefront, applying cutting-edge technologies, including AI and agentic tools, to solve real-world problems across US Corporate Functions, including Finance, Risk, Law, and AuditDrive the migration of .NET applications from on-premises to Azure, designing and building hybrid solutions that remain reliable, secure, and scalable across both environmentsProvide hands-on development and management of application integration plans, incorporating AI solutions where they add value, from conception through production deployment, with committed delivery timelines.Drive solution architecture and design for APIs, web services, SSO and security, and messaging (Kafka or MQ), with a focus on performance, security, and complianceBuild and maintain back-end APIs and web services, delivering heavy integration work across SSO and security, and messaging through Kafka or MQ.Drive and lead the integration of CNB entities, ensuring seamless business adoption of target solutions in collaboration with Target Tech teamsConsolidate applications from CNB Finance & Audit Tech to Target RBC Common Platform through strategic alignment with business stakeholdersCarry out substantial data work, including scripting (Python) and data pipeline developmentLead, mentor, and grow a team of engineers setting technical direction and quality standards.Maintain a working understanding of enterprise operations, including shared services, Splunk, Elastic, and performance monitoringChampion cross-functional collaboration by leading discussions with business stakeholders to gather requirements, refine visions, and align AI initiatives with strategic goals to translate into actionable AI strategiesDevelop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP, ensuring scalability, reliability, and efficiency through Kubernetes, Docker, etc.What do you need to succeed?
7+ years leading an engineering team, staying hands-on and stepping in to unblock delivery when neededSoftware engineering background with at least one of the following languages: .NET, Java, or PythonSolid experience building and integrating APIs and web services across SSO, security, and messaging through Kafka or MQ, with the ability to drive architecture and design decisions focused on performance, security, and complianceProven experience migrating applications from on-premises to cloud platforms (Azure, AWS, or GCP) and designing and running hybrid solutions across both environmentsSkilled at translating business requirements into technical roadmaps, and identify AI-driven transformation opportunities aligned with enterprise strategyStrong data skills, including experience with ETL, data orchestration, Python scripting, or building data pipelinesExperience developing and deploying cloud-native solutions using Kubernetes and Docker, with exposure to AI or ML solutions in productionExperience delivering integration plans to committed timelines, including managing architecture changesAbility to partner with enterprise and business stakeholders, gather requirements, and align technical work with strategic goalsNice to have:
Experience scaling and deploying AI applications in cloud environments with model optimization, latency reduction, and performance monitoringExperience with Retrieval‑Augmented Generation (RAG) systems and vector databases for knowledge retrievalSolid foundation in prompt engineering and AI based SDLC (Claude, GitHub Copilot)Familiarity with Python and AI frameworks (LangChain, AutoGen, CrewAI)Understanding of AI agent architectures, autonomous decision‑making systems, and multi-agent collaboration frameworksWhat's in it for you?
We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.A comprehensive Total Rewards Program including bonuses
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