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 Dev Ops 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 cutting-edge 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 Ui Path 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, Re Act, 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, Tensor Flow

📌 Lead Ai Engineer (Toronto)
🏢 RBC
📍 Toronto