21 hours ago
Toronto, CanadaStaff+
Responsibilities
- Design and develop MCP servers and tools for AI integration.
- Build LLM-powered automation, prompt-engineering, conversational AI, agentic automation, and advanced AI solutions.
- Develop ML model integration solutions, AI orchestration workflows, monitoring, and evaluation systems.
- Integrate UiPath with enterprise AI platforms and APIs.
- Implement Intelligent Document Processing and NLP solutions.
- Design and manage vector databases and RAG systems, including embeddings and retrieval strategies.
- Develop model evaluation, tuning, governance, and responsible AI frameworks.
- Integrate AI technologies into SDLC workflows, development tooling, code generation, testing, deployment, and monitoring.
- Evaluate enterprise software packages and AI platforms and recommend solutions to senior management.
- Lead and mentor colleagues through AI product reviews, pilot projects, technology adoption, training, and knowledge sharing.
- Analyze business requirements, conduct feasibility studies, and translate complex requirements into technical designs.
- Collaborate with enterprise architecture, security, compliance, engineering, business stakeholders, and senior management on AI integration.
- Drive adoption of AI-driven practices across engineering teams.
Requirements
- 10+ years of experience building and operating production software systems at scale.
- 2–4 years of hands-on experience with LLMs and generative AI in production environments.
- Expert proficiency in .NET/C#, Java, Python, SQL, Git, and CI/CD pipelines.
- Deep knowledge of MVC, MVVM, SOA, and software architecture concepts, with hands-on Azure Integration Services experience.
- Expert-level API architecture experience with REST and SOAP.
- Advanced knowledge of agentic AI patterns, including tool use, agent orchestration, ReAct, multi-step reasoning, memory, and context management.
- Proficiency integrating LLM APIs, prompt engineering, and RAG systems using vector databases, embeddings, and retrieval strategies.
- Demonstrated experience designing and building MCP servers and tools with agent-tool integration frameworks.
- Cloud platform expertise with AWS, Azure, or GCP, including Docker, Kubernetes, and CI/CD pipelines.
- Understanding of security, data governance, and responsible AI practices.
- Strong collaboration and communication skills, including code reviews, design discussions, pair programming, and communication with technical and business audiences.
- Preferred: production experience with agentic automation and advanced AI solutions.
- Preferred: open-source contributions to automation or AI/ML projects.
- Preferred: Microsoft Azure Developer Associate or Azure Solutions Architect Expert certification.
- Preferred: experience building and training regression, classification, or clustering models with Scikit-learn, TensorFlow, or PyTorch.
Benefits
- Comprehensive total rewards including competitive compensation, bonuses, flexible benefits, commissions, and stock options where applicable.
- Development-focused leadership, coaching, and career opportunities.
- Opportunity to make a meaningful impact on enterprise AI adoption.
- Dynamic, collaborative, and high-performing team environment.
- World-class training in financial services and emerging technologies.
- Full-time, regular employment in Toronto, Canada, with 37.5 work hours per week.
- Applications are accepted until November 27, 2026, at 11:59 PM on the preceding day.
Tech Stack
Categories
About RBC
Royal Bank of Canada (RBC) provides personal and commercial banking, payments, mortgages, investing, wealth management, insurance, and capital markets services to consumers, businesses, and institutions. The public company, headquartered in Toronto and founded in 1864, serves more than 17 million clients across Canada, the U.S., and other global markets. Revenue comes from interest income, fees, advisory, and trading across its diversified financial services businesses, and it is Canada’s largest bank by market capitalization.