Level

RBCPosted 2w ago

Lead AI Engineer

Lead AI Engineer at RBC scores 92 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

TORONTO, Ontario, CanadaleadFull time

AI in this role

pytorchtensorflowscikit-learn
prompt-engineeringragai-evaluationnlpai-safety

Job Description

Lead AI Engineer

What is the Opportunity?

We are seeking an accomplished Lead AI Engineer to design, develop, and implement AI-driven solutions within our software development lifecycle. You will lead enterprise-scale AI integration initiatives, establish best practices, and advance the technical maturity of our engineering organization. This role actively contributes to improving our Agile, Engineering, and DevOps practices.

This position demands exceptional problem-solving skills, deep AI expertise, and proficiency in modern development technologies (.NET/C#, Java, Python). You will effectively communicate and collaborate with engineering teams, business stakeholders, and senior management across multiple projects and organizational boundaries.

Role Level and Responsibilities

As a Lead AI Engineer, you are an expert professional with deep technical expertise who contributes to organizational objectives through innovative solutions to complex business challenges. Your primary focus is identifying root causes and implementing solutions to critical business problems while assessing and communicating the technological impact on the business.

Key Activities

  • Evaluate enterprise software packages and AI platforms, recommending solutions to senior management
  • Lead and mentor colleagues in AI product reviews, pilot projects, and technology adoption
  • Conduct in-depth analysis of business requirements for large-scale, mission-critical projects
  • Perform feasibility studies and translate complex requirements into robust technical designs
  • Drive adoption of AI-driven practices across engineering teams

What You'll Do

AI Engineering & Implementation

  • Lead the design and development of MCP (Model Context Protocol) servers and tools for seamless AI integration
  • Design and implement AI-driven automation solutions, including LLM-powered automation, prompt engineering, and conversational AI
  • Develop ML model integration solutions and AI orchestration workflows with monitoring and evaluation
  • Integrate UiPath with enterprise AI platforms and APIs
  • Support implementation of agentic automation and advanced AI use cases

AI/ML Expertise & Governance

  • Integrate LLM APIs and apply advanced prompt engineering techniques
  • Design and maintain AI workflow orchestration systems
  • Implement Intelligent Document Processing and NLP solutions
  • Develop model evaluation, tuning, and governance frameworks
  • Design and manage vector databases and RAG (Retrieval-Augmented Generation) systems
  • Support responsible AI practices and governance models

AI-Driven Software Development Lifecycle (SDLC)

  • Implement AI spec-driven development practices for your squad
  • Contribute to AI/ML solutions that enhance code generation, testing, deployment, and monitoring
  • Integrate AI technologies into existing SDLC workflows and development tooling
  • Collaborate on governance and responsible AI implementation

Technical Leadership & Collaboration

  • Lead technology evaluation and assessment for organizational adoption
  • Collaborate with enterprise architecture, security, and compliance teams on AI integration
  • Contribute to technical training and knowledge-sharing initiatives across the organization

What You Need to Succeed

Must Have

  • 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 design patterns (MVC, MVVM), SOA, and architectural concepts; hands-on development experience with Azure Integration Services
  • Expert-level expertise in API architecture (REST/SOAP)
  • Advanced working knowledge of agentic AI patterns: tool use, agent orchestration, ReAct, multi-step reasoning, memory and context management
  • Proven proficiency in LLM API integration, prompt engineering, and RAG systems (vector databases, embeddings, retrieval strategies)
  • Demonstrated experience designing and building MCP servers and tools with agent-tool integration frameworks
  • Cloud platform expertise (AWS, Azure, or GCP), including containers (Docker/Kubernetes) and CI/CD pipelines
  • Strong collaboration and communication skills; comfortable with code reviews, design discussions, and pair programming
  • Ability to communicate technical decisions clearly to both technical peers and business stakeholders
  • Understanding of security, data governance, and responsible AI practices

Nice-to-Have

  • Experience with agentic automation and advanced AI solutions in production
  • Open-source contributions to automation or AI/ML projects
  • Microsoft Azure certification (Azure Developer Associate or Azure Solutions Architect Expert)
  • Experience building and training machine learning models (regression, classification, clustering) using frameworks like Scikit-learn, TensorFlow, or PyTorch

What's In It For You?

We thrive on the challenge to be our best, embrace progressive thinking, and work together to deliver trusted solutions that help our clients and communities prosper. We are committed to supporting each other's growth and creating mutual success.

  • Comprehensive Total Rewards Program including competitive compensation, bonuses, flexible benefits, commissions, and stock options where applicable
  • Development-focused leadership committed to your growth through coaching and career opportunities
  • Opportunity to make a meaningful, lasting impact on enterprise AI adoption
  • Dynamic, collaborative, and high-performing team environment
  • World-class training programs in financial services and emerging technologies

#LI-POST

#TechPJ

Job Skills

Application Development, Application Integrations, Application Maintenance, Applications Architecture, Detail-Oriented, Enterprise Application Delivery, Group Problem Solving, Programming Languages, Software Development Life Cycle (SDLC), System Applications

Additional Job Details

Address:

180 WELLINGTON ST W:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-08-26

Application Deadline:

2026-10-09

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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Expand your limits and create a new future together at RBC. Find out how we use our passion and drive to enhance the well-being of our clients and communities at jobs.rbc.com.

RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRagAI EvaluationNlpAI SafetyPyTorchTensorFlowscikit-learn

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. How do you decide that one model's output is better than another's for a given task?
  4. What NLP problem have you worked on, and how did you measure whether it actually worked?
  5. How do you think about the risk of an AI system in this kind of role failing silently?

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering, Rag, AI Evaluation, Nlp, and AI Safety. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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