Lead, Full Stack Engineer
Role Overview
Hands-on leadership role in AI Engineering for US Functions Technology, covering finance, risk, law, and audit.
Build and inspire a team delivering scalable, production-ready solutions.
Lead .NET modernization, AI-enabled engineering, and CNB technology integration into RBC enterprise platforms.
Key Skills for This Role
Full Job Posting
Role overview
Hands-on leadership role in AI Engineering for US Functions Technology, covering finance, risk, law, and audit.
Build and inspire a team delivering scalable, production-ready solutions.
Lead .NET modernization, AI-enabled engineering, and CNB technology integration into RBC enterprise platforms.
Modernization and integration
- Migrate .NET applications from on-premises to Azure and design reliable, secure, scalable hybrid solutions.
- Own application integration plans from concept through production deployment, with committed timelines.
- Lead API and web service architecture, SSO and security, and Kafka or MQ messaging.
- Lead CNB entity integration and business adoption; consolidate CNB Finance and Audit applications onto the RBC common platform.
Leadership, data, and AI
- Lead, mentor, and grow engineers, setting technical direction and quality standards.
- Build backend APIs and web services and develop Python scripts and data pipelines.
- Partner with business stakeholders to gather requirements, shape roadmaps, and align AI initiatives with enterprise goals.
- Develop and deploy cloud-native AI/ML solutions using AWS, Azure, or GCP and Kubernetes or Docker.
- Maintain working understanding of enterprise operations, shared services, Splunk, Elastic, and performance monitoring.
Required qualifications
- 7+ years leading an engineering team while remaining hands-on.
- Software engineering background with at least one of .NET, Java, or Python.
- Experience building and integrating APIs and web services across SSO, security, and Kafka or MQ messaging.
- Experience migrating on-premises applications to cloud and designing hybrid solutions.
- Ability to translate business needs into technical roadmaps and identify AI transformation opportunities.
- Strong data skills in ETL, orchestration, Python scripting, or pipelines.
- Experience with cloud-native solutions, Kubernetes, Docker, and exposure to AI/ML in production.
- Experience delivering integration plans to timelines and managing architecture changes.
- Ability to partner with stakeholders, gather requirements, and align technical delivery to strategic goals.
Nice-to-have skills
- Scaling AI applications, model optimization, latency reduction, and performance monitoring.
- RAG systems, vector databases, prompt engineering, AI-based SDLC tools, and AI agent architectures or frameworks.
Employment and compensation
- Full-time, regular, salaried position; no compensation amount or pay cadence is stated.
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