AI Engineer

Design, build, and operate AI solutions that power RBC Retail Investments' digital experiences.,Partner with product, advisors, and technology teams to translate investment and client problems into AI/ML use cases.,Develop, test, and deploy models and LLM-based solutions (e.g., personalization, recommendations, insight generation, workflow automation) using modern cloud and MLOps practices.,Work end-to-end on the lifecycle from data exploration and feature engineering through to monitoring, performance tuning, and continuous improvement in production.,Embed responsible AI practices by focusing on fairness, explainability, compliance, and client trust.,Contribute to our AI platform and engineering standards, reusable components, and best practices to accelerate future use cases.,Strong AI/ML engineering skills – 3+ years building and deploying models or LLM-based solutions in production (Python, SQL, model serving, monitoring).,Solid software engineering foundations – clean code, testing, version control, CI/CD, and working in cloud environments (e.g., Azure/AWS/GCP).,End-to-end data & ML lifecycle experience – from problem framing and data discovery through feature engineering, training, evaluation, and MLOps.,Applied NLP/LLM experience – building use cases such as recommendations, summarization, Q&A, or workflow automation using modern AI tooling.

Must-have:,- 3+ years building and deploying models or LLM-based solutions in production (Python, SQL, model serving, monitoring).,- Solid software engineering foundations: clean code, testing, version control, CI/CD, and working in cloud environments (e.g., Azure/AWS/GCP).,- End-to-end data & ML lifecycle experience.,- Applied NLP/LLM experience.,Nice-to-have:,- Experience in wealth management, retail investing, or other regulated financial domains.,- Hands-on work with LLM ecosystems – RAG, vector databases, prompt engineering, guardrails, and evaluation frameworks.,- Background in experimentation and measurement – A/B testing, online metrics, causal inference, or uplift modelling.,- Familiarity with modern data & ML platforms – e.g., Databricks, Kubernetes, feature stores, or event-driven architectures.